Uplift modeling estimates individual-level treatment effects for personalized decisions in applications such as marketing and healthcare. Beyond binary treatments, many applications involve multi-valued treatments. As data are split across more groups, sparsity and imbalance pose challenges. We propose CAMU, which combines (i) a Codebook-enhanced representation network that anchors representations across mini-batches to stabilize learning and facilitate distribution alignment, and (ii) a Cross-Treatment Self-Attention (CTSA) enhanced prediction network for treatment-aware knowledge sharing. We extend CAMU to CAMUER, which transfers unbiased signals from randomized controlled trial (RCT) data via attention alignment and prediction-consistency distillation. The proposed model is deployed in Kuaishou's ad-serving system, serving tens of millions of users. Offline experiments and a large-scale online A/B test demonstrate consistent gains over baselines.

The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowledge base. Moreover, domain documents are characterized by dense terminology and strong contextual dependencies, which exacerbate the semantic fragmentation of text chunks, thereby making it difficult to efficiently utilize their key information. To address these challenges, this paper proposes QChunker, which restructures the RAG paradigm from retrieval-augmentation to understanding-retrieval-augmentation. Firstly, QChunker models the text chunking as a composite task of text segmentation and knowledge completion to ensure the logical coherence and integrity of text chunks. Drawing inspiration from Hal Gregersen's ''Questions Are the Answer'' theory, we design a multi-agent debate framework comprising four specialized components: a question outline generator, text segmenter, integrity reviewer, and knowledge completer. This framework operates on the principle that questions serve as catalysts for profound insights. Through this pipeline, we successfully construct a high-quality dataset of 45K entries and transfer this capability to small language models. Additionally, to handle long evaluation chains and low efficiency in existing chunking evaluation methods, which overly rely on downstream QA tasks, we introduce a novel direct evaluation metric, ChunkScore. Both theoretical and experimental validations demonstrate that ChunkScore can directly and efficiently discriminate the quality of text chunks. Furthermore, during the text segmentation phase, we utilize document outlines for multi-path sampling to generate multiple candidate chunks and select the optimal solution employing ChunkScore. Extensive experimental results across four heterogeneous domains exhibit that QChunker effectively resolves aforementioned issues by providing RAG with more logically coherent and information-rich text chunks. Notably, this study also establishes a small-domain QA dataset concerning hazardous chemical safety, which fully reveals the significant value of RAG in specialized domains and the generalization capability of the QChunker framework.

Audio-driven human gesture synthesis is a crucial task with broad applications in virtual avatars, human-computer interaction, and creative content generation. Despite notable progress, existing methods often produce coarse gestures, lack expressiveness, and fail to fully align with audio semantics. To address these challenges, we propose ExGes, a novel retrieval-enhanced diffusion framework with three key designs: (1) a Motion Base Construction, which builds a gesture library from the training dataset; (2) a Motion Retrieval Module, employing contrastive learning and momentum distillation for retrieving fine-grained reference poses; and (3) a Precise Control Module, integrating partial masking and stochastic masking to enable flexible and fine-grained control. Experimental evaluations on BEAT2 demonstrate that ExGes reduces Fréchet Gesture Distance by 4.55%and improves motion diversity by 5.3% over EMAGE, with user studies revealing a 71.3% preference for its naturalness and semantic relevance.

In face-to-face conversations, individuals need to switch between speaking and listening roles seamlessly. Existing 3D talking head generation models focus solely on speaking or listening, neglecting the natural dynamics of interactive conversation, which leads to unnatural interactions and awkward transitions. To address this issue, we propose a new task—multi-round dual-speaker interaction for 3D talking head generation—which requires models to handle and generate both speaking and listening behaviors in continuous conversation. To solve this task, we introduce DualTalk, a novel unified framework that integrates the dynamic behaviors of speakers and listeners to simulate realistic and coherent dialogue interactions. This framework not only synthesizes lifelike talking heads when speaking but also generates continuous and vivid non-verbal feedback when listening, effectively capturing the interplay between the roles. We also create a new dataset featuring 50 hours of multi-round conversations with over 1,000 characters, where participants continuously switch between speaking and listening roles. Extensive experiments demonstrate that our method significantly enhances the naturalness and expressiveness of 3D talking heads in dual-speaker conversations. We recommend watching the supplementary video: https://ziqiaopeng.github.io/dualtalk

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements.

Offline Reinforcement Learning (RL) optimizes policy using pre-collected data instead of direct environment interaction, offering a safe and cost-effective solution for sequential decision-making in the real world. However, it faces challenges such as distribution shift issues and vulnerability under perturbations. Researchers have developed various conservative methods to improve the robustness of offline RL. Nevertheless, model-based methods can result in transition distribution shift issues, while model-free value-based uncertainty penalty methods may not be sufficiently robust. To address these problems, we propose a new method called Robust Offline RL via Conservative Smoothing and Dynamics Controlling (RCSD). To achieve reliable value estimation of out-of-distribution (OOD) actions, RCSD uses both model-free uncertainty penalty and model-based simulation methods. It introduces a new one-step simulation method with conservative dynamics controlling to avoid value overestimation caused by transition distribution shifts. Moreover, RCSD considers both current and next states when generating OOD states to ensure cautious value estimation and efficient data utilization. RCSD uses conservative Q-smoothing and policy smoothing to strengthen the policy against sudden changes under perturbations. Experiments on D4RL benchmark demonstrate that RCSD can achieve state-of-the-art performance compared to baselines in either benchmark or adversarial attack tests.

Mapping-based methods are critical solutions for Cross-Domain Cold Start Recommendation problem, which learns a mapping function to transfer knowledge from the source domain to the target domain based on overlap users between domains. However, in many CDCSR scenarios, there exists selection bias in observed overlap users due to the users being free to choose whether to rate or interact with items of a specific domain. This selection bias without proper treatment can introduce bias to the model learning process.To address the selection bias in observed overlap users, we propose two estimators for CDCSR: Inverse Propensity Weighting CDCSR Estimator (IPW-CDCSRE) and Doubly Robust CDCSR Estimator (DR-CDCSRE). IPW-CDCSRE learns propensity scores to adjust for selection bias and enhances user representations through the learned propensity model. DR-CDCSRE further employs an imputation model to consider prediction loss for non-overlap users. Additionally, we introduce the Recurrent Learning Process (RLP) to enhance the stability and effectiveness of DR-CDCSRE. To validate the effectiveness and generalization ability of the proposed estimators, we conducted extensive experiments on three real-world CDCSR scenarios, utilizing four base CDCSR models and two types of loss functions.

Audio-driven 3D face animation is crucial for live streaming and augmented reality, yet most existing methods focus on specific individuals with predefined speaking styles, limiting adaptability to varied styles. To address this, we introduce MetaFace, a novel methodology for speaking style adaptation based on meta-learning. MetaFace comprises three key components: the Robust Meta Initialization Stage (RMIS) for foundational style adaptation, the Dynamic Relation Mining Neural Process (DRMN) to connect observed and unobserved speaking styles, and a Low-rank Matrix Memory Reduction Approach to optimize model efficiency and style detail learning. These innovations enable MetaFace to significantly outperform existing baselines and set a new state-of-the-art, as demonstrated by our experimental results.

Cross-domain recommendation (CDR) is a critical solution to address the sparsity issue of conventional recommendation systems. In most scenarios, only a subset of users have interactions on both domains, which is referred to as partial user overlap CDR. Despite this, most CDR methods tend to design alignment mechanisms only for overlapping users, resulting in partial alignment issue. In this paper, we analyze the cause of partial alignment, including the limited expressive capacity of the models and the lack of alignment signal for non-overlapping users. To address this issue, we propose a new model DACDR (Diffusion Alignment Cross-domain recommendation) with two specially designed modules: a user latent diffusion module and a user alignment module. The user latent diffusion module integrates a diffusion model into the CDR process to enhance the expressive capacity of the model. The user alignment module introduces novel alignment mechanisms that consider both overlapping and non-overlapping users. We conduct extensive experiments on three CDR scenarios to evaluate the performance of DACDR. Our results demonstrate that DACDR outperforms the baselines.

Click-through rate (CTR) prediction lies at the heart of the online advertising ecosystem and recommendation systems, helping to improve user engagement and platform revenue. With the advent of Pretrained Language Models (PLMs), researchers focus on incorporating textual features to enhance semantic understanding in CTR prediction. However, existing methods typically aggregate a wealth of textual features and encode the informative text into a single semantic embedding. This mechanism leads to entangled embedding that fails to capture fine-grained feature interactions, ultimately limiting CTR prediction performance. To address this issue, we propose Multi-faceted Semantic Disentanglement for CTR prediction (MSD-CTR), a novel framework designed to disentangle and leverage multi-faceted textual information. MSD-CTR consists of two key components: Disentangled Semantic Topic Model (DSTopic) and Topic Guided Disentangled Representation Learning (TopicDRL). DSTopic employs a disentangled generative process to extract multi-faceted knowledge from the entangled textual information. Meanwhile, TopicDRL integrates the extracted multi-faceted knowledge into CTR prediction and introduces two alignment losses to guide disentangled semantic embedding learning. Extensive experiments on four real-world datasets demonstrate that MSD-CTR outperforms existing CTR models, highlighting the effectiveness of disentangling textual information for better click-through rate prediction.

Music-Driven Dance Generation seeks to create dance movements synchronized with music, playing a key role in applications like performance and gaming. While solo dance generation has seen progress, group dance generation remains underexplored. Although several methods have been proposed, existing approaches frequently fail to ensure spatial-temporal coherence, resulting in unrealistic and aesthetically unpleasing performances. To tackle the issue, we introduce CoheDancers, a novel framework for Music-Driven Interactive Group Dance Generation. CoheDancers aims to enhance group dance generation coherence by decomposing it into three key aspects: synchronization, naturalness, and fluidity. Correspondingly, we develop a Cycle Consistency based Dance Synchronization strategy to foster music-dance correspondences, an Auto-Regressive-based Exposure Bias Correction strategy to enhance the fluidity of the generated dances, and an Adversarial Training Strategy to augment the naturalness of the group dance output. Collectively, these strategies enable CoheDancers to produce highly coherent group dances with superior quality. Furthermore, to establish better benchmarks for Group Music2Dance, we construct the most diverse and comprehensive open-source dataset to date, I-Dancers, featuring rich dancer interactions, and create comprehensive evaluation metrics. Experimental evaluations on I-Dancers and other extant datasets substantiate that CoheDancers achieves unprecedented state-of-the-art performance. Code is available at https://github.com/XulongT/CoheDancers.

Lip synchronization is the task of aligning a speaker’s lip movements in video with corresponding speech audio, and it is essential for creating realistic, expressive video content. However, existing methods often rely on reference frames and masked-frame inpainting, which limit their robustness to identity consistency, pose variations, facial occlusions, and stylized content. In addition, since audio signals provide weaker conditioning than visual cues, lip shape leakage from the original video will affect lip sync quality. In this paper, we present OmniSync, a universal lip synchronization framework for diverse visual scenarios. Our approach introduces a mask-free training paradigm using Diffusion Transformer models for direct frame editing without explicit masks, enabling unlimited-duration inference while maintaining natural facial dynamics and preserving character identity. During inference, we propose a flow-matching-based progressive noise initialization to ensure pose and identity consistency, while allowing precise mouth-region editing. To address the weak conditioning signal of audio, we develop a Dynamic Spatiotemporal Classifier-Free Guidance (DS-CFG) mechanism that adaptively adjusts guidance strength over time and space. We also establish the AIGC-LipSync Benchmark, the first evaluation suite for lip synchronization in diverse AI-generated videos. Extensive experiments demonstrate that OmniSync significantly outperforms prior methods in both visual quality and lip sync accuracy, achieving superior results in both real-world and AI-generated videos.

Music-driven 3D dance generation has attracted increasing attention in recent years, with promising applications in choreography, virtual reality, and creative content creation. Previous research has generated promising realistic dance movement from audio signals. However, traditional methods underutilize genre conditioning, often treating it as auxiliary modifiers rather than core semantic drivers. This oversight compromises music-motion synchronization and disrupts dance genre continuity, particularly during complex rhythmic transitions, thereby leading to visually unsatisfactory effects. To address the challenge, we propose MEGADance, a novel architecture for music-driven 3D dance generation. By decoupling choreographic consistency into dance generality and genre specificity, MEGADance demonstrates significant dance quality and strong genre controllability. It consists of two stages: (1) High-Fidelity Dance Quantization Stage (HFDQ), which encodes dance motions into a latent representation by Finite Scalar Quantization (FSQ) and reconstructs them with kinematic-dynamic constraints, and (2) Genre-Aware Dance Generation Stage (GADG), which maps music into the latent representation by synergistic utilization of Mixture-of-Experts (MoE) mechanism with Mamba-Transformer hybrid backbone. Extensive experiments on the FineDance and AIST++ dataset demonstrate the state-of-the-art performance of MEGADance both qualitatively and quantitatively. Code is available at https://github.com/XulongT/MEGADance.

Achieving high synchronization in the synthesis of realistic, speech-driven talking head videos presents a significant challenge. Traditional Generative Adversarial Networks (GAN) struggle to maintain consistent facial identity, while Neural Radiance Fields (NeRF) methods, although they can address this issue, often produce mismatched lip movements, inadequate facial expressions, and unstable head poses. A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expression, and head poses. The absence of these synchronizations is a fundamental flaw, leading to unrealistic and artificial outcomes. To address the critical issue of synchronization, identified as the “devil” in creating realistic talking heads, we introduce SyncTalk. This NeRF-based method effectively maintains subject identity, enhancing synchronization and realism in talking head synthesis. SyncTalk employs a Face-Sync Controller to align lip movements with speech and innovatively uses a 3D facial blendshape model to capture accurate facial expressions. Our Head-Sync Stabilizer optimizes head poses, achieving more natural head movements. The Portrait-Sync Generator restores hair details and blends the generated head with the torso for a seamless visual experience. Extensive experiments and user studies demonstrate that SyncTalk outperforms state-of-the-art methods in synchronization and realism. We recommend watching the supplementary video: https://ziqiaopeng.github.io/synctalk

In this paper, we present PoseRec, an innovative approach aimed at enhancing online advertisement recommendations for micro-videos to boost click-through rates. Addressing the inherent background bias introduced via direct video content learning from image frames, we exploit rich data within the 3D human pose. PoseRec capitalizes on the merits of 3D human pose detection and multi-frame pose data, resulting in superior advertisement recommendation performance. Additionally, we introduce a unique item-aware implicit prototype learning module and a pose-aware transductive hard-negative mining module to tackle the issues of ambiguity and sparsity in advertisement recommendation. Upon evaluation on our novel dataset, Pose-OBE, our method exhibits robust performance surpassing strong baselines, corroborating its effectiveness in resolving the complex challenges of micro-video advertisement recommendation.

In the realm of category-level 6D object pose estimation, canonical 3D representation reconstruction is pivotal, yet current methods show limitations in reconstruction quality, a key step in current pose estimation pipeline. To address this, we introduce an innovative Adversarial Canonical Representation Reconstruction Network (ACR-Pose) in this paper. In particular, ACR-Pose comprises a Reconstructor, with novel sub-modules: a Pose-Irrelevant Module (PIM) for robustness to rotation and translation, and a Relational Reconstruction Module (RRM) for extracting relational information between input modalities. A Discriminator is incorporated to guide the generation of realistic canonical representations through adversarial optimization. Evaluated on the prevalent NOCS-CAMERA and NOCS-REAL datasets, our method significantly improves the performance of baseline models and achieves comparable performance with existing state-of-the-art methods, representing a promising advancement in the field of category-level 6D object pose estimation.

Dance and music are intimately interconnected, with group dance being a crucial part of dance artistry. Consequently, Music-Driven Group Dance Generation has been a fundamental and challenging task in various fields like education, art, and sports. However, existing methods fail to fully explore group dance coherence. Thus, we propose CoDancers, a novel and efficient retrieval-based music-driven group dance generation framework. CoDancers improves performance by decomposing group dance coherence into individual movement coherence and group interaction coherence for specialized design, incorporating a Spatial-Temporal Group Dance Blender block, a Acoustic-Semantic Music Miner block, and a Stereotype-Reducing Dance Generator block. Experimental results on the public dataset demonstrate the superiority of our method over existing baselines, achieving state-of-the-art performance. The code is available at https://github.com/XulongT/CoDancers.

Dance and music are closely related forms of expression, with mutual retrieval between dance videos and music being a fundamental task in various fields like education, art, and sports. However, existing methods often suffer from unnatural generation effects or fail to fully explore the correlation between music and dance. To overcome these challenges, we propose BeatDance, a novel beat-based model-agnostic contrastive learning framework. BeatDance incorporates a Beat-Aware Music-Dance InfoExtractor, a Trans-Temporal Beat Blender, and a Beat-Enhanced Hubness Reducer to improve Music-Dance retrieval performance by utilizing the alignment between music beats and dance movements. We also introduce the Music-Dance (M-D) dataset, a large-scale collection of over 10,000 Music-Dance video pairs for training and testing. Experimental results on the M-D dataset demonstrate the superiority of our method over existing baselines, achieving state-of-the-art performance. The code and dataset are available at https://github.com/XulongT/BeatDance.

Scene Graph Generation is a critical enabler of environmental comprehension for autonomous robotic systems.Most of existing methods, however, are often thwarted by the intricate dynamics of background complexity, which limits their ability to fully decode the inherent topological information of the environment.Additionally, the wealth of contextual information encapsulated within depth cues is often left untapped, rendering existing approaches less effective.To address these shortcomings, we present STDG, an avant-garde Depth-Guided One-Stage Scene Graph Generation methodology.The innovative architecture of STDG is a triad of custom-built modules: The Depth Guided HHA Representation Generation Module, the Depth Guided Semi-Teaching Network Learning Module, and the Depth Guided Scene Graph Generation Module.This trifecta of modules synergistically harnesses depth information, covering all aspects from depth signal generation and depth feature utilization, to the final scene graph prediction.Importantly, this is achieved without imposing additional computational burden during the inference phase.Experimental results confirm that our method significantly enhances the performance of onestage scene graph generation baselines.

Cross-domain recommendation (CDR) methods achieve success in disentangling user preferences into domain-specific and domain-shared parts. However, recent research has shown that isolated domain-specific preference limits performance improvements. In this paper, we propose a new CDR framework, called CausalCDR, which identifies the limitations of existing methods and addresses existing issues. CausalCDR consists of two views: the causal view and the generative view. The causal view incorporates causality of variables into the CDR scenario, while the generative view implements the causal view by modeling the joint distribution of user interaction via encoding, causal, and generation stage. To optimize CausalCDR, we re-derive the Evidence Lower Bound (ELBO) and introduce a mutual information regularizer and an adversarial classifier. We evaluate CausalCDR on four real-world CDR scenarios and demonstrate its effectiveness in improving CDR performance.

The tagging system has become a primary tool to organize information resources on the Internet, which benefits both users and the platforms. To build a successful tagging system, automatic tagging methods are desired. With the development of society, new tags keep emerging. The problem of tagging items with emerging tags is an open challenge for an automatic tagging system, and it has not been well studied in the literature. We define this problem as a tag-centered cold-start problem in this study and propose a novel neural topic model based few-shot learning method named NTFSL to solve the problem. In our proposed method, we innovatively fuse the topic modeling task with the few-shot learning task, endowing the model with the capability to infer effective topics to solve the tag-centered cold-start problem with the property of interpretability. Meanwhile, we propose a novel neural topic model for the topic modeling task to improve the quality of inferred topics, which helps enhance the tagging performance. Furthermore, we develop a novel inference method based on the variational auto-encoding framework for model inference. We conducted extensive experiments on two real-world datasets, and the results demonstrate the superior performance of our proposed model compared with state-of-the-art machine learning methods. Case studies also show the interpretability of the model.

Speech-driven 3D face animation aims to generate realistic facial expressions that match the speech content and emotion. However, existing methods often neglect emotional facial expressions or fail to disentangle them from speech content. To address this issue, this paper proposes an end-to-end neural network to disentangle different emotions in speech so as to generate rich 3D facial expressions. Specifically, we introduce the emotion disentangling encoder (EDE) to disentangle the emotion and content in the speech by cross-reconstructed speech signals with different emotion labels. Then an emotion-guided feature fusion decoder is employed to generate a 3D talking face with enhanced emotion. The decoder is driven by the disentangled identity, emotional, and content embeddings so as to generate controllable personal and emotional styles. Finally, considering the scarcity of the 3D emotional talking face data, we resort to the supervision of facial blendshapes, which enables the reconstruction of plausible 3D faces from 2D emotional data, and contribute a large-scale 3D emotional talking face dataset (3D-ETF) to train the network. Our experiments and user studies demonstrate that our approach outperforms state-of-the-art methods and exhibits more diverse facial movements. We recommend watching the supplementary video: https://ziqiaopeng.github.io/emotalk

Large-scale place recognition is a fundamental but challenging task, which plays an increasingly important role in autonomous driving and robotics. Existing methods have achieved acceptable good performance, however, most of them are concentrating on designing elaborate global descriptor learning network structures. The importance of feature generalization and descriptor post-enhancing has long been neglected. In this work, we propose a novel method named GIDP to learn a Good Initialization and Inducing Descriptor Pose-enhancing for Large-scale Place Recognition. In particular, an unsupervised momentum contrast point cloud pretraining module and a reranking-based descriptor post-enhancing module are proposed respectively in GIDP. The former aims at learning a good initialization for the point cloud encoding network before training the place recognition model, while the later aims at post-enhancing the predicted global descriptor through reranking at inference time. Ex-tensive experiments on both indoor and outdoor datasets demonstrate that our method can achieve state-of-the-art performance using simple and general point cloud encoding backbones.

Real-world sensors often produce incomplete, irregular, and noisy point clouds, making point cloud completion increasingly important. However, most existing completion methods rely on large paired datasets for training, which is labor-intensive. This paper proposes RaPD, a novel semi-supervised point cloud completion method that reduces the need for paired datasets. RaPD utilizes a two-stage training scheme, where a deep semantic prior is learned in stage 1 from unpaired complete and incomplete point clouds, and a semi-supervised prior distillation process is introduced in stage 2 to train a completion network using only a small number of paired samples. Additionally, a self-supervised completion module is introduced to improve performance using unpaired incomplete point clouds. Experiments on multiple datasets show that RaPD outperforms previous methods in both homologous and heterologous scenarios.

Speech-driven 3D face animation technique, extending its applications to various multimedia fields.Previous research has generated promising realistic lip movements and facial expressions from audio signals. However, traditional regression models solely driven by data face several essential problems, such as difficulties in accessing precise labels and domain gaps between different modalities, leading to unsatisfactory results lacking precision and coherence.To enhance the visual accuracy of generated lip movement while reducing the dependence on labeled data, we propose a novel framework SelfTalk, by involving self-supervision in a cross-modals network system to learn 3D talking faces. The framework constructs a network system consisting of three modules: facial animator, speech recognizer, and lip-reading interpreter. The core of SelfTalk is a commutative training diagram that facilitates compatible features exchange among audio, text, and lip shape, enabling our models to learn the intricate connection between these factors. The proposed framework leverages the knowledge learned from the lip-reading interpreter to generate more plausible lip shapes. Extensive experiments and user studies demonstrate that our proposed approach achieves state-of-the-art performance both qualitatively and quantitatively. We recommend watching the supplementary video.

Simultaneous Localization and Mapping (SLAM) and Autonomous Driving are becoming increasingly more important in recent years. Point cloud-based large scale place recognition is the spine of them. While many models have been proposed and have achieved acceptable performance by learning short-range local features, they always skip long-range contextual properties. Moreover, the model size also becomes a serious shackle for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net. On top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer (CSVT) are proposed respectively to learn both short-range local features and long-range contextual features. Consisting of ASVT and CSVT, SVT-Net can achieve state-of-the-art performance in terms of both recognition accuracy and running speed with a super-light model size (0.9M parameters). Meanwhile, for the purpose of further boosting efficiency, we introduce two simplified versions, which also achieve state-of-the-art performance and further reduce the model size to 0.8M and 0.4M respectively.

Utilizing computer vision technologies for machinery missing part detection has been a hot research topic recently. Most of existing methods take images as input and utilize 2D object detection pipelines for detecting fault regions. However, 2D models can’t handle the situation when occlusion exists. To tackle the issue, in this paper, we propose a novel model named MPDNet, which exploits 3D point cloud pairs as input for missing part detection. In MPDNet, the missing part detection problem is transformed into a binary segmentation problem. The key idea behind our approach is that difference between two point clouds can be fully perceived in the latent feature space if they are encoded by the same encoder. Specially, we firstly propose a shared encode and abnormal lift module to find and enlarge difference between the target point cloud to be diagnosed and its corresponding predefined source point cloud without any fault. Then an attention based decoder is proposed to segment the source point cloud into two clusters: points that are missing in the target point cloud and points that are preserved in the target point cloud. Meanwhile, to make the model robust, a point cloud construction module is proposed as an auxiliary task to help the shared encoder to learn more discriminative features. Experiments on both synthetic and real world datasets have demonstrated the effectiveness of our model.

Tags have been adopted by many online services as a method to manage their online resources. Effective tagging benefits both users and firms. In real applications providing a user tagging mechanism, only a small portion of tags are usually provided by users. Therefore, an automatic tagging method, which can assign tags to different items automatically, is urgently needed. Previous works on automatic tagging focus on exploring the tagging behavior of users or the content information of items. In online service platforms, users frequently browse items related to their interests, which implies users’ judgment about the underlying features of items and is helpful for automatic tagging. Browsing-behavior records are much more plentiful compared with tagging behavior and easy to collect. However, existing studies about automatic tagging ignore this kind of information. To properly integrate both browsing behaviors and content information for automatic tagging, we propose a novel probabilistic graphical model and develop a new algorithm for the model parameter inference. We conduct thorough experiments on a real-world data set to evaluate and analyze the performance of our proposed method. The experimental results demonstrate that our approach achieves better performance than state-of-the-art automatic tagging methods. Summary of Contribution. In this paper, we study how to automatically assign tags to items in an e-commerce background. Our study is about how to perform item tagging for e-commerce and other online service providers so that consumers can easily find what they need and firms can manage their resources effectively. Specifically, we study if consumer browsing behavior can be utilized to perform the tagging task automatically, which can save efforts of both firms and consumers. Additionally, we transform the problem into how to find the most proper tags for items and propose a novel probabilistic graphical model to model the generation process of tags. Finally, we develop a variational inference algorithm to learn the model parameters, and the model shows superior performance over competing benchmark models. We believe this study contributes to machine learning techniques.

Multivariate time series forecasting is very important for many applications. Many studies have been conducted for accurate and interpretable prediction methods. However, existing methods either cannot take both times series and covariates into consideration, lacking of interpretability, or ignore global trends across multivariate time series. In this paper, we aim to solve these issues. To this end, we propose a new model named TEDGE for accurate and interpretable time series prediction. In this model, we extract global trends hidden across multivariate times series to improve prediction accuracy. Meanwhile, we utilize a deep recurrent model with attention mechanism to find long-and short-term sequential patterns hidden in individual time series with interpretability. We conduct experiments on several datasets to evaluate the proposed models performance. Results demonstrate the superior performance of our proposed model.

3D object detection is crucial to ensure the reliability and stability of autonomous driving systems. In recent years, researchers have made great progress in 3D object detection by combining features of images and point clouds. However, there is still much room for detection accuracy improvement, especially for small object detection. In this paper, we propose a frustum-based 3D object detection model named PointFPN. The core idea of PointFPN is learning expressive semantic and contextual information for small objects, e.g., pedestrians and cyclists in an urban street scene. To detect 3D objects, our model uses frustums to bridge the gap between images and point clouds and thus generating proposals. Then, a feature pyramid structure is designed to extract and fuse multi-level features of target objects represented by point clouds. Meanwhile, we develop a multilevel regression network to calculate different parameters of 3D bounding boxes at different feature levels. Through elaborate structure designed above, our model can learn discriminative features which are highly relevant to bounding box parameters at different feature levels. Experimental study shows that our model is effective in detecting small objects and has a strong robustness to sparse point clouds. Our model demonstrates state-of-the-art performance on small object detection on KITTI benchmark.

Point cloud based retrieval for place recognition remains to be a problem demanding prompt solution due to its difficulty in efficiently encoding local features into adequate global descriptor in scenes. Existing studies solve this problem by generating a global descriptor for each point cloud, which is used to retrieve matched point cloud in database. But existing studies do not make effective use of the relationship between points and neglect different feature's discrimination power. In this paper, we propose to employ Dual Attention and Graph Convolution for point cloud based place recognition (DAGC) to solve these issues. Specifically, we employ two modules to help extract discriminative and generalizable features to describe a point cloud. We introduce a Dual Attention module to help distinguish task-relevant features and to utilize other points' different contributions to a point to generate representation. Meanwhile, we introduce a Residual Graph Convolution Network (ResGCN) module to aggregate local features of each point's multi-level neighbor points to further improve the representation. In this way, we improve the descriptor generation by considering the importance of both point and feature and leveraging point relationship. Experiments conducted on different datasets show that our work outperforms current approaches on all evaluation metrics.

Abstract Point cloud based 3D scene recognition is fundamental to many real world applications such as Simultaneous Localization and Mapping (SLAM). However, most of existing methods do not take full advantage of the contextual semantic features of scenes. And their recognition abilities are severely affected by dynamic noise such as points of cars and pedestrians in the scene. To tackle these issues, we propose a new Scene Recognition Network, namely SRNet. In this model, to learn local features without being affected by dynamic noise, we propose Static Graph Convolution (SGC) module, which are then stacked as our backbone. Next, to further avoid dynamic noise, we introduce a Spatial Attention Module (SAM) to make the feature descriptor pay more attention to immovable local areas that are more relevant to our task. Finally, in order to make a more profound sense of the scene, we design a Dense Semantic Fusion (DSF) strategy to integrate multi‐level features during feature propagation, which helps the model deepen its understanding of the contextual semantics of scenes. By utilizing these designs, SRNet can map scenes to discriminative and generalizable feature vectors, which are then used for finding matching pairs. Experimental studies demonstrate that SRNet achieves new state‐of‐the‐art on scene recognition and shows good generalization ability to other point cloud based tasks.

Abstract Point cloud based 3D vision tasks, such as 3D object recognition, are critical to many real world applications such as autonomous driving. Many point cloud processing models based on deep learning have been proposed by researchers recently. However, they are all large‐sample dependent, which means that a large amount of manually labelled training data are needed to train the model, resulting in huge labor cost. In this paper, to tackle this problem, we propose a One‐Shot learning model for Point Cloud Recognition, namely OS‐PCR. Different from previous methods, our method formulates a new setting, where the model only needs to see one sample per class once for memorizing at inference time when new classes are needed to be recognized. To fulfill this task, we design three modules in the model: an Encoder Module, an Edge‐conditioned Graph Convolutional Network Module, and a Query Module. To evaluate the performance of the proposed model, we build a one‐shot learning benchmark dataset for 3D point cloud analysis. Then, comprehensive experiments are conducted on it to demonstrate the effectiveness of our proposed model.

Abstract User tags in social network are valuable information for many applications such as Web search, recommender systems and online advertising. Thus, extracting high quality tags to capture user interest has attracted many researchers’ study in recent years. Most previous studies inferred users’ interest based on text posted in social network. In some cases, ordinary users usually only publish a small number of text posts and text information is not related to their interest very much. Compared with famous user, it is more challenging to find non-famous (ordinary) user’s interest. In this paper, we propose a probabilistic topic model,Bi-Labeled LDA,to automatically find interest tags for non-famous users in social network such as Twitter. Instead of extracting tags from text posts, tags of non-famous users are inferred from interest topics of famous users. With the proposed model, the formulation of social relationship between non-famous users and famous user is simulated and interest tags of famous users are exploited to supervise the training of the model and to make use of latent relation among famous users. Furthermore, the influence of popularity of famous user and popular tags are considered, and tags of non-famous users are ranked based on random walk model. Experiments were conducted on Twitter real datasets. Comparison with state-of-the-art methods shows that our method is more superior in terms of both ranking and quality of the tagging results.

With the development of scientific research, scientific publications are valuable resources for new-comers in the research field. But massive scientific publications make it a challenge for researchers diving into a new research field. As a good practice to this problem, topics are put forward to organize publications. In this paper, we propose two modified LDA topic models as solutions to topic analysis and influential paper discovery on scientific publications, cc-LDA and cp-LDA. Compared to state-of-the-art researches on LDA, we incorporate citation information including its occurrence times and occurrence position into our models. Model cc-LDA integrates paper content and citation occurrence into LDA model, while cp-LDA considers both occurrence and position of citations. Both models can not only find topics in the form of citation distribution, but also help discover influential papers under certain topics. Furthermore, both models can extract more representative vectors for papers, which achieve good performance in subsequent clustering.

In the era of the social web, many people manage their social relationships through various online social networking services. It has been found that identifying the types of social relationships among users in online social networks facilitates the marketing of products via electronic “word of mouth.” However, it is a great challenge to identify the types of social relationships, given very limited information in a social network. In this article, we study how to identify the types of relationships across multiple heterogeneous social networks and examine if combining certain information from different social networks can help improve the identification accuracy. The main contribution of our research is that we develop a novel decision tree initiated random walk model, which takes into account both global network structure and local user behavior to bootstrap the performance of relationship identification. Experiments conducted based on two real‐world social networks, Sina Weibo and Jiepang, demonstrate that the proposed model achieves an average accuracy of 92.0%, significantly outperforming other baseline methods. Our experiments also confirm the effectiveness of combining information from multiple social networks. Moreover, our results reveal that human mobility features indicating location categories, coincidence, and check‐in patterns are among the most discriminative features for relationship identification.

With the widespread usage of smart phones, more and more mobile apps are developed every day, playing an increasingly important role in changing our lifestyles and business models. In this trend, it becomes a hot research topic for developing effective mobile app recommender systems in both industry and academia. Compared with existing studies about mobile app recommendations, our research aims to improve the recommendation effectiveness based on analyzing a psychological trait of human beings, exploratory behavior, which refers to a type of variety-seeking behavior in unfamiliar domains. To this end, we propose a novel probabilistic model named Goal-oriented Exploratory Model (GEM), integrating exploratory behavior identification with personalized item recommendation. An algorithm combining collapsed Gibbs sampling and Expectation Maximization is developed for model learning and inference. Through extensive experiments conducted on a real dataset, the proposed model demonstrates superior recommendation performances and good interpretability compared with state-of-art recommendation methods. Moreover, empirical analyses on exploratory behavior find that individuals with a strong exploratory tendency exhibit behavioral patterns of variety seeking, risk taking, and higher involvement. Besides, mobile apps that are less popular or in the long tail possess greater potential of arousing exploratory behavior in individuals.

It has been well established that adding domain information about whether certain data objects (for example customers in customer segmentation application) should belong to the same segment can improve the quality of the segments. However, it can be expensive to acquire such domain knowledge. Consequently, we need to limit the number of constrains we acquire and more importantly maximize the effectiveness of these limited number of constraints we can acquire from the experts. Many of the constrained clustering methods randomly select constraints, which have been shown in our experiments to be ineffective. In this paper, we define the problem of identifying the most informative constraints. We propose an algorithm which generates the most informative constraints by maximizing the information gain from the constraints. We conducted a set of experiments on various data sets to compare our method with two other methods according to two measurements: the accuracy rate and the Vector Quantization Error (VQE), which is the objective function k-means clustering method minimizes. We illustrated that our approach not only achieves better accuracy rates, but also maintains low VQE values. Our results suggest that businesses can enhance their segmentation quality greatly by actively acquire the right type of domain information.

Inferring interests of users in social network is important for many applications such as personalized search, recommender systems and online advertising. Most previous studies inferred users' interests based on text posted in social network, which is usually not related to their interests. In this paper, we propose a modified topic model, Bi-Labeled LDA with a term weighting scheme, to extract interest tags for users in social network. The proposed model utilize only users' relationship information without requirement for text information, and incorporates supervision into traditional LDA. Specifically, we introduce method to extract tags for non-famous user through their relationship with famous users in Twitter, and study why a non-famous user follows famous users simultaneously. Comparison with state-of-the-art methods on real dataset shows that our method is far more superior in terms of precision and recall of the extracted tag set, and also more applicable for many personalized applications. Besides, we find that a reasonable term weighting scheme can actually improve the performance further.

Online user reviews are important information for both consumers and vendors. More and more people make their purchase decisions based on online reviews. Vendors also pay more and more attention to online reviews. However, as the number of reviews increases rapidly, the information overload problem prevents us making full use of online reviews. In this paper, we study how to find a representative set of high quality reviews to cover diversified aspects of user opinions. Existing work cannot solve this problem well. To overcome the drawbacks of existing methods, we define a new problem of finding the minimum set of reviews to cover all of features with different sentiment polarity and high quality without user-defined parameters. To solve the problem efficiently, we define potential objective function and develop greedy algorithm to find the solution in polynomial-time with approximation guarantee. We also propose two strategies to further reduce the number of reviews and to prune the search space respectively. Comprehensive experiments conducted on real review sets show that the proposed methods are effective and outperform existing methods.

Understanding differences between groups in a data set is one of the fundamental tasks in data analysis. As relevant applications accumulate, data-mining methods have been developed to specifically address the problem of group difference detection. Contrast set mining discovers group differences in the form of conjunction of feature-value pairs or items. In this paper, we incorporate absolute difference, relative difference, and statistical significance in our definition of a group difference, and develop a novel method named DIFF that uses the prefix-tree structure to compress the search space, follows a tree traversal procedure to discover the complete set of significant group differences, and employs efficient pruning strategies to expedite the search process. We conducted comprehensive experiments to compare our method with existing methods on completeness of results, pruning efficiency, and computational efficiency. The experiments demonstrate that our method guarantees completeness of results and achieves higher pruning efficiency and computational efficiency compared to STUCCO. In addition, our definition of group difference is more general than STUCCO. Our method is more effective than traditional approaches, such as classification trees, in discovering the complete set of significant group differences.

In the online world, customers can easily navigate to different online stores to make purchases. The products purchased on one site are often associated with product purchases on other sites (e.g., a hotel reservation on one site and a car rental on another site). Whereas market basket analysis is often used to discover associations among products for brick-and-mortar stores, it is rarely applied in the online setting where consumers navigate among different online stores to buy products. We define online shopping patterns and develop two novel methods to perform market basket analysis across websites. While this research is motivated by online shopping applications, our contribution is mainly methodological. The two methods we develop in this paper can not only be used to identify various online shopping patterns across sites and products but can also be applied to settings where patterns exist across different dimensions. Experiments on both synthetic data and real online shopping data demonstrate the effectiveness of our methods.

Measuring similarity between objects is a fundamental task in domains such as data mining, information retrieval, and so on. Link-based similarity measures have attracted the attention of many researchers and have been widely applied in recent years. However, most previous works mainly focus on introducing new link-based measures, and seldom provide theoretical as well as experimental comparisons with other measures. Thus, selecting the suitable measure in different situations and applications is difficult. In this paper, a comprehensive analysis and critical comparison of various link-based similarity measures and algorithms are presented. Their strengths and weaknesses are discussed. Their actual runtime performances are also compared via experiments on benchmark data sets. Some novel and useful guidelines for users to choose the appropriate link-based measure for their applications are discovered.

We address the discovery of periodic patterns in sequence data. Building on prior work in this area, we present definitions and new methods for characterizing and identifying four types of periodic patterns. A unifying concept across the different types of periodic patterns we consider is the use of statistical variance to define periodicity. This lends itself to efficient variance-reduction algorithms for identifying periodic patterns. We motivate and test our approach using both extensive simulated sequences and real sequence data from online clickstream data.

Recent years have witnessed increased efforts on detecting topics and events from Web search logs, since this kind of data not only capture web content but also reflect the users’ activities. However, the majority of existing work is focused on exploiting clustering techniques for topic and event detection. Due to the huge size and the evolving nature of Web data, existing clustering approaches are limited to meet the real-time demand. To that end, in this article, we propose a method called LETD to detect evolving topics in a timely manner. Also, we design the techniques to extract events from topics and to infer the evolving relationship among the events. For topic detection, we first provide a measurement to select the important URLs, which are most likely to describe a real-life topic. Then, starting from these selected URLs, we exploit the local expansion method to find other topic-related URLs. Moreover, in the LETD framework, we design algorithms based on Random Walk and Markov Random Fields (MRF), respectively. Because the LETD method exploits a divide-and-conquer strategy to process the data, it is more efficient than existing methods based on clustering techniques. To better illustrate the LETD framework, we develop a demo system StoryTeller which can discover hot topics and events, infer the evolving relationships among events, and visualize information in a storytelling way. This demo system can provide a global view of the topic development and help users target the interesting events more conveniently. Finally, experimental results on real-world Microsoft click-through data have shown that StoryTeller can find real-life hot topics and meaningful evolving relationships among events, and has also demonstrated the efficiency and effectiveness of the LETD method.

Search engine users often have clear search tasks hidden behind their queries. Inspired by this, the modern search engines are providing an increasing number of services to help users simplify their key tasks. However, the problem of what are the major user search tasks with high traffic for which search engines should design special services is still underexplored. In this paper, we propose a novel Multi-view Random Walk (MRW) algorithm to measure the search task oriented similarity between queries, and then group search queries with similar tasks so that the major search tasks of users can be identified from search engine click-through log. The proposed MRW, which is a general framework to combine knowledge from different views in a random walk process, allows the random surfer to walk across different views to integrate information for search task discovery. Experimental results on click-through log of a commonly used commercial search engine show that our proposed MRW algorithm can effectively discover user search tasks.

Simplifying the key tasks of search engine users by directly retrieving to them structured knowledge according to their queries is attracting much attention from both industry and academia. A bottleneck of this challenging problem is how to extract the structured knowledge from the noisy and complex Web scale websites automatically. In this paper, we propose an unsupervised automatic wrapper induction algorithm, named as Scalable Knowledge Extractor from webSites (SKES). SKES induces the wrapper in a divide and conquer mode, i.e., it divides the general wrapper into several sub-wrappers to learn from the data independently. Moreover, through employing techniques such as tag path representation of Web pages, SKES is verified to be efficient and noise-tolerant by the experimental results. Furthermore, based on our automatically extracted knowledge, we also built a prototype to serve structured knowledge to end users for simplifying their key search tasks. Very positive feedbacks were received on the prototype.

SimRank is a well known algorithm which conducts link analysis to measure similarity between each pair of nodes (nodepair). But it suffers from high computational cost, limiting its usage in large-scale datasets. Moreover, Links between nodes are changing over time. It may be desirable to quickly approximate the similarity score between certain nodepair without performing a large-scale computation on the entire graph. In our approach we propose a method to efficiently estimate the similarity score using only a small subgraph of the entire graph. We call this novel algorithm “Local-SimRank”. The experimental results conducted on real datasets and synthetic dataset show that our algorithm efficiently produces good approximations to the global SimRank scores. Meanwhile, we prove that the Local-SimRank score LS(a, b) is always less than original SimRank score S(a, b) mathematically.

SimRank is an intuitive and effective measure for link-based similarity that scores similarity between two nodes as the first-meeting probability of two random surfers, based on the random surfer model. However, when a user queries the similarity of a given node-pair based on SimRank, the existing approaches need to compute the similarities of other node-pairs beforehand, which we call an all-pair style. In this paper, we propose a Single-Pair SimRank approach. Without accuracy loss, this approach performs an iterative computation to obtain the similarity of a single node-pair. The time cost of our Single-Pair SimRank is always less than All-Pair SimRank and obviously efficient when we only need to assess similarity of one or a few node-pairs. We confirm the accuracy and efficiency of our approach in extensive experimental studies over synthetic and real datasets.

Feature selection is an essential data processing step to remove irrelevant and redundant attributes for shorter learning time, better accuracy, and better comprehensibility. A number of algorithms have been proposed in both data mining and machine learning areas. These algorithms are usually used in a single table environment, where data are stored in one relational table or one flat file. They are not suitable for a multi‐relational environment, where data are stored in multiple tables joined to one another by semantic relationships. To address this problem, in this article, we propose a novel approach called FARS to conduct both Feature And Relation Selection for efficient multi‐relational classification. Through this approach, we not only extend the traditional feature selection method to select relevant features from multi‐relations, but also develop a new method to reconstruct the multi‐relational database schema and eliminate irrelevant tables to improve classification performance further. The results of the experiments conducted on both real and synthetic databases show that FARS can effectively choose a small set of relevant features, thereby enhancing classification efficiency and prediction accuracy significantly.

We investigate a well-known algorithm, Space-Saving [Metwally et al. 2006], which has been proven efficient and effective at mining frequent elements in data streams. We discovered an error in one of the theorems in Metwally et al. [2006]. Experiments are conducted to illustrate the error.

Recently, video search reranking has been an effective mechanism to improve the initial text-based ranking list by incorporating visual consistency among the result videos. While existing methods attempt to rerank all the individual result videos, they suffer from several drawbacks. In this article, we propose a new video reranking paradigm called cluster-based video reranking (CVR). The idea is to first construct a video near-duplicate graph representing the visual similarity relationship among videos, followed by identifying the near-duplicate clusters from the video near-duplicate graph, then ranking the obtained near-duplicate clusters based on cluster properties and intercluster links, and finally for each ranked cluster, a representative video is selected and returned. Compared to existing methods, the new CVR ranks clusters and exhibits several advantages, including superior reranking by utilizing more reliable cluster properties, fast reranking on a small number of clusters, diverse and representative results. Particularly, we formulate the near-duplicate cluster identification as a novel maximally cohesive subgraph mining problem. By leveraging the designed cluster scoring properties indicating the cluster's importance and quality, random walk is applied over the near-duplicate cluster graph to rank clusters. An extensive evaluation study proves the novelty and superiority of our proposals over existing methods.

Influence between objects needs to be assessed in many applications. Lots of measures have been proposed, but a domain-independent method is still expected. In this paper, we give a probabilistic definition of influence based on the random walker model on graphs. Two approaches, linear systems method and Basic InfRank algorithm, are shown and return equal results, but Basic InfRank is more efficient by iterative computation. Two variants on bipartite graphs and star graphs are discussed. Experiments show InfRank algorithms have good accuracy, fast convergent rate and high performance.

Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in characterizing the object similarities in networks, such as the Web, by exploiting the object-to-object relationship. Unfortunately, it is prohibitively expensive to compute the link-based similarity in a relatively large graph. In this paper, based on the observation that link-based similarity scores of real world graphs follow the power-law distribution, we propose a new approximate algorithm, namely Power-SimRank, with guaranteed error bound to efficiently compute link-based similarity measure. We also prove the convergence of the proposed algorithm. Extensive experiments conducted on real world datasets and synthetic datasets show that the proposed algorithm outperforms SimRank by four-five times in terms of efficiency while the error generated by the approximation is small.

In this paper, we present a system called CRO (Chinese Review Observer) for online product review structurization. By Structurization, we mean identifying, extracting and summarizing information from unstructured review text to a structured table. The core tasks include review collection, product feature and user opinion extraction, and polarity analysis of opinions. Existing research in this area is mainly English text oriented. To deal with Chinese effectively, we propose several novel approaches for fulfilling the core tasks. Then we integrated these approaches and implement the whole procedure of review structurization in the system CRO. Running results for reviews of real products show its performance is satisfactory.

Decision tree induction is one of the widely used classification approaches. It constructs a tree in which an internal node is split based on values of a selected attribute. Depending on the attribute selected at each level of the tree, a training dataset could lead to many different trees. Consequently, it is possible that an unseen case is classified into different and conflict classes using different trees. On the other band, recently developed association role based classifications are able to generate more interesting and useful rules than decision trees. However, the large number of rules without appropriate data structure brings the issue of efficiency. In this paper, we propose to unify decision tree classification with association-based classification using generalized decision trees (GDT). GDT generalizes the concept of decision tree to encode all interesting classification rules discovered based on association in a tree form. it inherits the merits of both approaches and removes their drawbacks. The data structure and related algorithms are discussed. Results of an experimental study are presented to indicate the advantages of GDT.