Multimodal Multi-label Emotion Recognition (MMER) aims to identify human emotions through various modalities. Previous studies mainly focus on aligning cross-modal data to extract discriminative emotion-dependent features using attention or reconstruction-based strategies, while omitting the fact that the MMER task is also subjected to the multi-label noises that exist in the multi-label classifications, which disturb the modality-to-label correlations. Besides, most of the research also failed to balance the strategy of finding internal label correlations and label dependency of modalities in noisy conditions. In this paper, we proposed a novel Heterogeneous Graph Embedding (HGE) method for the MMER task, which exploits heterogeneous graphs to extract emotional commonality in the modal and temporal levels, and explicitly models cross-modal correlations among heterogeneous modalities. Additionally, it also captures uncertainty brought by multi-label noise and leverages the unevenness of multi-label to overcome potential data issues. Experimental results demonstrate that our HGE method achieves state-of-the-art performance on two widely used multimodal multi-label emotion recognition datasets under both noise-free and noisy circumstances.

Event retrieval involves searching for specific events from untrimmed video galleries and has garnered significant attention in recent years. However, most existing works follow a text-based video retrieval paradigm only, limited by two main drawbacks: (1) The episodic information presented in described events is not fully perceived, leading to declines in retrieval performance facing variable query intentions. (2) Current models are prone to returning false positive results with similar semantics, as simple text queries can hardly accurately describe the target video content users seek. In this paper, we propose a novel event retrieval framework termed Composed Query-Based Event Retrieval (CQBER). Specifically, we first construct two CQBER benchmark datasets, namely ActivityNet-CQ and TVR-CQ, which cover TV shows and open-world scenarios, respectively. Additionally, we propose an initial CQBER method, termed Multimodal Episodic Perceptron (MEP), which excavates complete query semantics from both observed static visual cues and various descriptions. Extensive experiments demonstrate that our proposed framework significantly boosts event retrieval accuracy across different existing methods. Our code and datasets are available at https://github.com/VincentVanNF/CQBER.

Compositional temporal grounding (CTG) aims to localize the most relevant segment from an untrimmed video based on a given natural language sentence, and the test samples for this task contain novel components not seen in training. However, existing CTG methods suffer from two shortcomings: (1) Most methods adopt transformers to model global video information only, thus failing to balance the long-range perception and regional representation of video sequences; (2) Due to the lack of aligning videos and sentences at a fine-grained level, the model's capacity for compositional generalization is limited, particularly when query sentences contain novel components. To address these problems, we propose a novel method called Principal Token-aware Adjacent Network (PTAN), which consists of three parts: (1) Principal Temporal Token Recomposition combining video clip-level features obtained from the transformer backbone to capture more significant local features while retaining enough contextual information. (2) Regional Semantic-Aware Learning, which exploits regional representations of videos for cross-modal semantic alignment on the feature space. (3) Principal Semantic-Aware Learning that facilitates fine-grained alignment between visual and textual by sensing principal visual and textual tokens in a self-supervised manner. Extensive experiments on two widely used benchmarks (i.e., Charades-CG and ActivityNet-CG) show that our PTAN method outperforms recent CTG state-of-the-art methods, achieving remarkable improvements in compositional generalization. Our code is available at https://github.com/rushzy/PTAN.

Text-based person retrieval is one of the fundamental tasks in the field of computer vision, which aims to retrieve the most relevant pedestrian image from all the candidates according to textual descriptions. Such a cross-modal retrieval task could be challenging since it requires one to properly select distinguishing clues and perform cross-modal alignments. To achieve cross-modal alignments, most previous works focus on different inter-modal constraints while overlooking the influence of intra-modal noise, yielding sub-optimal retrieved results in certain cases. To this end, we propose a novel framework termed Multi-granularity Separation Network with Bidirectional Refinement Regularization (MSN-BRR) to tackle the problem. The framework consists of two components: (1) Multi-granularity Separation Network, which extracts the multi-grained discriminative textual and visual representations at local and global semantic levels. (2) Bidirectional Refinement Regularization, which alleviates the influence of intra-modal noise and facilitates the proper alignments between the visual and textual representations. Extensive experiments on two widely used benchmarks, i.e., CUHK-PEDES and ICFG-PEDES show that our MSN-BRR method outperforms current state-of-the-art methods.

Zero-shot sketch-based image retrieval (ZS-SBIR) is a task that learns semantic knowledge and embedding extraction to retrieve similar images using a sketch without any training examples of unseen classes. Existing methods have attempted to address the modal and semantic gaps in ZS-SBIR by using various strategies such as leveraging category linguistic information for improved discriminability and utilizing knowledge distillation to increase the model’s generalizability towards unseen classes. However, these methods fail to consider the importance of discriminability and generalizability in a unified manner. To address this, we propose a novel method called Adaptive Balanced Discriminability and Generalizability (ABDG) for ZS-SBIR. Specifically, our ABDG method utilizes an advanced two-stage knowledge distillation scheme to balance the learning of discriminability and generalizability for each instance. In addition to task-agnostic teacher models to preserve structural information used in existing work, we introduce a task-specific teacher model pre-trained with a classification objective function to emphasize the discriminability property during knowledge distillation. We also employ a novel entropy-based weighting strategy to balance the effects of structural information preservation and classification losses specific to the classification progress of each instance. Furthermore, we use fine-grained semantic relevance to refine the ego predictions of the student model, with the aim of improving its performance as the training objective continues to converge. Experimental results on three benchmark datasets of ZS-SBIR demonstrate that our ABDG method establishes a state-of-the-art performance by balancing the learning of discriminative and generalizable properties.

We propose the Accelerated Sign Hunter (ASH), a sign-based black-box attack under l∞ constraint. The proposed method searches an approximate gradient sign of loss w.r.t. the input image with few queries to the target model and crafts the adversarial example by updating the input image in this direction. It applies a Branch-Prune Strategy that infers the unknown sign bits according to the checked ones to avoid unnecessary queries. It also adopts a Stabilized Hierarchical Search to achieve better performance within a limited query budget. We provide a theoretical proof showing that the Accelerated Sign Hunter halves the queries without dropping the attack success rate (SR) compared with the state-of-the-art sign-based black-box attack. Extensive experiments also demonstrate the superiority of our ASH method over other black-box attacks. In particular on Inception-v3 for ImageNet, our method achieves the SR of 0.989 with an average queries of 338.56, which is 1/4 fewer than that of the state-of-the-art sign-based attack to achieve the same SR. Moreover, our ASH method is out-of-the-box since there are no hyperparameters that need to be tuned.

In skeleton-based action recognition, Graph Convolutional Networks (GCNs) have achieved remarkable performance since the skeleton representation of human action can be naturally modeled by the graph structure. Most of the existing GCN-based methods extract skeleton features by exploiting single-scale joint information, while neglecting the valuable multi-scale contextual information. Besides, the commonly used strided convolution in temporal dimension could evenly filters out the keyframes we expect to preserve and leads to the loss of keyframe information. To address these issues, we propose a novel Selective Hypergraph Convolution Network, dubbed Selective-HCN, which stacks two key modules: Selective-scale Hypergraph Convolution (SHC) and Selective-frame Temporal Convolution (STC). The SHC module represents the human skeleton as the graph and hypergraph to fully extract multi-scale information, and selectively fuse features at various scales. Instead of traditional strided temporal convolution, the STC module can adaptively select keyframes and filter redundant frames according to the importance of the frames. Extensive experiments on two challenging skeleton action benchmarks, i.e., NTU-RGB+D and Skeleton-Kinetics, demonstrate the superiority and effectiveness of our proposed method.

Zero-Shot Cross-Modal Retrieval (ZS-CMR) has recently drawn increasing attention as it focuses on a practical retrieval scenario, i.e., the multimodal test set consists of unseen classes that are disjoint with seen classes in the training set. The recently proposed methods typically adopt the generative model as the main framework to learn a joint latent embedding space to alleviate the modality gap. Generally, these methods largely rely on auxiliary semantic embeddings for knowledge transfer across classes and unconsciously neglect the effect of the data reconstruction manner in the adopted generative model. To address this issue, we propose a novel ZS-CMR model termed Multimodal Disentanglement Variational AutoEncoders (MDVAE), which consists of two coupled disentanglement variational autoencoders (DVAEs) and a fusion-exchange VAE (FVAE). Specifically, DVAE is developed to disentangle the original representations of each modality into modality-invariant and modality-specific features. FVAE is designed to fuse and exchange information of multimodal data by the reconstruction and alignment process without pre-extracted semantic embeddings. Moreover, an advanced counter-intuitive cross-reconstruction scheme is further proposed to enhance the informativeness and generalizability of the modality-invariant features for more effective knowledge transfer. The comprehensive experiments on four image-text retrieval and two image-sketch retrieval datasets consistently demonstrate that our method establishes the new state-of-the-art performance.

In recent years, the Internet has stimulated the explosion of multimedia data. Food-related cooking videos, images, and recipes promote the rapid development of food computing. Image-recipe retrieval is an important sub-task in the field of cross-modal retrieval, which focuses on the measurement of the association between food image and recipe (title, ingredients, instructions). Although the existing methods have proposed some feasible solutions to achieve the goal of Image-recipe retrieval, there are still the following issues: 1) complex model structure and time-consuming training process. 2) the lack of information interaction within modalities and information integration between images and recipes. To this end, we propose a novel lightweight framework namedIntra- and Inter-Modality Hybrid Fusion (IMHF). Our IMHF model abandons a separate deep vision encoder and utilizes the transformer module to unify the visual and text features. In this way, valuable information from images and recipes can be condensed and the direct information interaction between the two modalities can be promoted. Both the intra- and inter-modality fusion can be realized. Extensive experiment results on the large-scale benchmark dataset Recipe1M demonstrate that our model IMHF with a lightweight architecture is superior to the state-of-the-art approaches.

In recent years, as the fundamental task in video understanding, Temporal Action Detection is attracting extensive attention. Most existing approaches use the same model parameters to process all input videos, which are not adaptive to the input video during the inference stage. In this paper, we propose a novel model termed Multi-scale Dynamic Network (MDN) to tackle this problem. The proposed MDN model incorporates multiple Multi-scale Dynamic Modules (MDMs). Each MDM can generate video-specific and segment-specific convolution kernels based on video content from different scales and adaptively capture rich semantic information for the prediction. Besides, we also design a new Edge Suppression Loss (ESL) function for MDN to pay more attention to hard examples. Extensive experiments conducted on two popular benchmarks ActivityNet-1.3 and THUMOS-14 show that the proposed MDN model achieves the state-of-the-art performance.

Image-recipe retrieval, which aims at retrieving the relevant recipe from a food image and vice versa, is now attracting widespread attention, since sharing food-related images and recipes on the Internet has become a popular trend. Existing methods have formulated this problem as a typical cross-modal retrieval task by learning the image-recipe similarity. Though these methods have made inspiring achievements for image-recipe retrieval, they may still be less effective to jointly incorporate the three crucial points: (1) the association between ingredients and instructions, (2) fine-grained image information, and (3) the latent alignment between recipes and images. To this end, we propose a novel framework namedHybrid Fusion with Intra- and Cross-Modality Attention (HF-ICMA) to learn accurate image-recipe similarity. Our HF-ICMA model adopts an intra-recipe fusion module to focus on the interaction between ingredients and instructions within a recipe, and further enriches the expressions of the two separate embeddings. Meanwhile, an image-recipe fusion module is devised to explore the potential relationship between fine-grained image regions and ingredients from the recipe, which jointly forms the final image-recipe similarity from both the local and global aspects. Extensive experiments on the large-scale benchmark dataset Recipe1M show that our model significantly outperforms the state-of-the-art approaches on various image-recipe retrieval scenarios.

Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression. Nevertheless, existing approaches could hardly guarantee a satisfactory performance with the extremely low-bit (e.g., 4-bit) hash codes due to the severe information loss and the shrink of the discrete solution space. In this article, we propose a novelCollaborative Learningstrategy that is tailored for generating high-quality low-bit hash codes. The core idea is to jointly distill bit-specific and informative representations for a group of pre-defined code lengths. The learning of short hash codes among the group can benefit from the manifold shared with other long codes, where multiple views from different hash codes provide the supplementary guidance and regularization, making the convergence faster and more stable. To achieve that, an asymmetric hashing framework with two variants of multi-head embedding structures is derived, termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of training and querying. Extensive experiments on three benchmark datasets have been conducted to verify the superiority of the proposed MAH, and have shown that the 8-bit hash codes generated by MAH achieve 94.3 percent of the MAP11.Mean Average Precision (MAP)score on the CIFAR-10 dataset, which significantly surpasses the performance of the 48-bit codes by the state-of-the-arts in image retrieval tasks.

Hashing methods have been extensively applied to efficient multimedia data indexing and retrieval on account of the explosion of multimedia data. Cross-modal hashing usually learns binary codes by mapping multi-modal data into a common Hamming space. Most supervised methods utilize relation information like class labels as pairwise similarities of cross-modal data pair to narrow intra-modal and inter-modal gap. In this paper, we propose a novel supervised cross-modal hashing method dubbed Subspace Relation Learning for Cross-modal Hashing (SRLCH), which exploits relation information of labels in semantic space to make similar data from different modalities closer in the low-dimension Hamming subspace. SRLCH preserves the modality relationships, the discrete constraints and nonlinear structures, while admitting a closed-form binary codes solution, which effectively enhances the training efficiency. An iterative alternative optimization algorithm is developed to simultaneously learn both hash functions and unified binary codes. With these binary codes and hash functions, we can index multimedia data and search them in an efficient way. Evaluations in two cross-modal retrieval tasks on several widely-used datasets show that the proposed SRLCH outperforms most cross-modal hashing methods. Theoretical analysis also illustrates reasons for our method’s promotion in subspace relation learning.

The availability of labeled image datasets has been shown critical for high-level image understanding, which continuously drives the progress of feature designing and models developing. However, constructing labeled image datasets is laborious and monotonous. To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries. We aim at collecting diverse and accurate images for given queries from the Web. Specifically, we formulate noisy textual queries removing and noisy images filtering as a multi-view and multi-instance learning problem separately. Our proposed approach not only improves the accuracy but also enhances the diversity of the selected images. To verify the effectiveness of our proposed approach, we construct an image dataset with 100 categories. The experiments show significant performance gains by using the generated data of our approach on several tasks, such as image classification, cross-dataset generalization, and object detection. The proposed method also consistently outperforms existing weakly supervised and web-supervised approaches.

Cross-modal retrieval, e.g., using an image query to search related text and vice-versa, has become a highlighted research topic, to provide flexible retrieval experience across multi-modal data. Existing approaches usually consider the so-called non-extendable cross-modal retrieval task. In this task, they learn a common latent subspace from a source set containing labeled instances of image-text pairs and then generate common representation for the instances in a target set to perform cross-modal matching. However, these method may not generalize well when the instances of the target set contains unseen classes since the instances of both the source and target set are assumed to share the same range of classes in the non-extensive cross-modal retrieval task. In this paper, we consider a more practical issue of extendable cross-modal retrieval task where instances in source and target set have disjoint classes. We propose a novel framework, termed Modal-adversarial Semantic Learning Network (MASLN), to tackle the limitation of existing methods on this practical task. Specifically, the proposed MASLN consists two subnetworks of cross-modal reconstruction and modal-adversarial semantic learning. The former minimizes the cross-modal distribution discrepancy by reconstructing each modality data mutually, with the guidelines of class embeddings as side information in the reconstruction procedure. The latter generates semantic representation to be indiscriminative for modalities, while to distinguish the modalities from the common representation via an adversarial learning mechanism. The two subnetworks are jointly trained to enhance the cross-modal semantic consistency in the learned common subspace and the knowledge transfer to instances in the target set. Comprehensive experiment on three widely-used multi-modal datasets show its effectiveness and robustness on both non-extendable and extendable cross-modal retrieval task.

The past decade has witnessed the boom of human-machine interactions, particularly via dialog systems. In this paper, we study the task of response generation in open-domain multi-turn dialog systems. Many research efforts have been dedicated to building intelligent dialog systems, yet few shed light on deepening or widening the chatting topics in a conversational session, which would attract users to talk more. To this end, this paper presents a novel deep scheme consisting of three channels, namely global, wide, and deep ones. The global channel encodes the complete historical information within the given context, the wide one employs an attention-based recurrent neural network model to predict the keywords that may not appear in the historical context, and the deep one trains a Multi-layer Perceptron model to select some keywords for an in-depth discussion. Thereafter, our scheme integrates the outputs of these three channels to generate desired responses. To justify our model, we conducted extensive experiments to compare our model with several state-of-the-art baselines on two datasets: one is constructed by ourselves and the other is a public benchmark dataset. Experimental results demonstrate that our model yields promising performance by widening or deepening the topics of interest.

Precisely recommending relevant multimedia items from massive candidates to a large number of users is an indispensable yet difficult task on many platforms. A promising way is to project users and items into a latent space and recommend items via the inner product of latent factor vectors. However, previous studies paid little attention to the multimedia content itself and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Content-aware Multimedia Recommendation Model with Graph Autoencoder (GraphCAR), combining informative multimedia content with user-item interaction. Specifically, user-item interaction, user attributes and multimedia contents (e.g., images, videos, audios, etc.) are taken as input of the autoencoder to generate the item preference scores for each user. Through extensive experiments on two real-world multimedia Web services: Amazon and Vine, we show that GraphCAR significantly outperforms state-of-the-art techniques of both collaborative filtering and content-based methods.

In our daily life, how to match clothing well is always a troublesome problem especially when we are shopping online to select a pair of matched pieces of clothing from tens of thousands available selections. To help common customers overcome selection issues, recent studies in the recommender system area have started to infer the fashion matching results automatically. The traditional fashion recommendation is normally achieved by considering visual similarity of clothing items or/and item co-purchase history from existing shopping transactions. Due to the high complexity of visual features and the lack of historical item purchase records, most of the existing work is unlikely to make an efficient and accurate recommendation. To address the problem, in this paper, we propose a new model called Discrete Supervised Fashion Coordinates Hashing. Its main objective is to learn meaningful yet compact high-level features of clothing items, which are represented as binary hash codes. In detail, this learning process is supervised by a clothing matching matrix, which is initially constructed based on limited known matching pairs and subsequently on the self-augmented ones. The proposed model jointly learns the intrinsic matching patterns from the matching matrix and the binary representations from the clothing items’ images, where the visual feature of each clothing item is discretized into a fixed-length binary vector. The binary representation learning significantly reduces the memory cost and accelerates the recommendation speed. The experiments compared with several state-of-the-art approaches have evidenced the superior performance of the proposed approach on efficient fashion recommendation.

Hashing techniques have recently gained increasing research interest in multimedia studies. Most existing hashing methods only employ single features for hash code learning. Multiview data with each view corresponding to a type of feature generally provides more comprehensive information. How to efficiently integrate multiple views for learning compact hash codes still remains challenging. In this article, we propose a novel unsupervised hashing method, dubbed multiview discrete hashing (MvDH), by effectively exploring multiview data. Specifically, MvDH performs matrix factorization to generate the hash codes as the latent representations shared by multiple views, during which spectral clustering is performed simultaneously. The joint learning of hash codes and cluster labels enables that MvDH can generate more discriminative hash codes, which are optimal for classification. An efficient alternating algorithm is developed to solve the proposed optimization problem with guaranteed convergence and low computational complexity. The binary codes are optimized via the discrete cyclic coordinate descent (DCC) method to reduce the quantization errors. Extensive experimental results on three large-scale benchmark datasets demonstrate the superiority of the proposed method over several state-of-the-art methods in terms of both accuracy and scalability.

Recently, a new type of video understanding task called Movie-Fill-in-the-Blank (MovieFIB) has attracted many research attentions. Given a pair of movie clip and description with one blank word as input, MovieFIB aims to automatically predict the blank word. Because of the advantage in processing sequence data, Long-Short Term Memory (LSTM) has been used as a key component in existing MovieFIB methods to generate representations of videos and descriptions. However, most of these methods fail to emphasize the salient parts of videos. To address this problem, in this paper we propose to use a novel LSTM network called LSTM with Linguistic gate (LSTMwL), which exploits adaptive temporal attention for MovieFIB. Specifically, we first use LSTM to produce video features, which are then used to update the text representation. Finally, we put the updated text into two opposite directional LSTMwL layers to infer the blank word. Experimental results demonstrate that our approach outperforms state-of-the-art models for MovieFIB.

Zero-shot learning (ZSL) aims to bridge the knowledge transfer via available semantic representations (e.g., attributes) between labeled source instances of seen classes and unlabelled target instances of unseen classes. Most existing ZSL approaches achieve this by learning a projection from the visual feature space to the semantic representation space based on the source instances, and directly applying it to the target instances. However, the intrinsic manifold structures residing in both semantic representations and visual features are not effectively incorporated into the learned projection function. Moreover, these methods may suffer from the inherent projection shift problem, due to the disjointness between seen and unseen classes. To overcome these drawbacks, we propose a novel framework termed transductive visual-semantic embedding (TVSE) for ZSL. In specific, TVSE first learns a latent embedding space to incorporate the manifold structures in both labeled source instances and unlabeled target instances under the transductive setting. In the learned space, each instance is viewed as a mixture of seen class scores. TVSE then effectively constructs the relational mapping between seen and unseen classes using the available semantic representations, and applies it to map the seen class scores of the target instances to their predictions of unseen classes. Extensive experiments on four benchmark datasets demonstrate that the proposed TVSE achieves competitive performance compared with the state-of-the-arts for zero-shot recognition and retrieval tasks.

Travel route planning aims to mine user's attributes and recommend personalized routes. How to build interest model for users and understand their real intention brings great challenges. This paper presents an approach which mines the user interest model by multi-source social media (e.g., travelogues and check-in records), and understands the user's real intention by active behavior such as point of interest (POI) inputs. In order to unify heterogeneous data from different sources, a topical package is built as the measurement space. Based on the topical package, user topical package is modeled to find user interest and route topical package is constructed to describe the attributes of each route. User's active behavior can also be considered during route planning, where top ranked routes are finally recommended. The proposed multi-source topical package (MSTP) approach is evaluated on a real dataset and compared with two state-of-the-art methods. The result shows that MSTP performs better for providing personalized travel routes.

Social media has become one of the most credible sources for delivering messages, breaking news, as well as events. Predicting the future dynamics of an event at a very early stage is significantly valuable, e.g, helping company anticipate marketing trends before the event becomes mature. However, this prediction is non-trivial because a) social events always stay with "noise'' under the same topic and b) the information obtained at its early stage is too sparse and limited to support an accurate prediction. In order to overcome these two problems, in this paper, we design an event early embedding model (EEEM) that can 1) extract social events from noise, 2) find the previous similar events, and 3) predict future dynamics of a new event. Extensive experiments conducted on a large-scale dataset of Twitter data demonstrate the capacity of our model on extract events and the promising performance of prediction by considering both volume information as well as content information.

This paper proposes a generic formulation that significantly expedites the training and deployment of image classification models, particularly under the scenarios of many image categories and high feature dimensions. As the core idea, our method represents both the images and learned classifiers using binary hash codes, which are simultaneously learned from the training data. Classifying an image thereby reduces to retrieving its nearest class codes in the Hamming space. Specifically, we formulate multiclass image classification as an optimization problem over binary variables. The optimization alternatingly proceeds over the binary classifiers and image hash codes. Profiting from the special property of binary codes, we show that the sub-problems can be efficiently solved through either a binary quadratic program (BQP) or a linear program. In particular, for attacking the BQP problem, we propose a novel bit-flipping procedure which enjoys high efficacy and a local optimality guarantee. Our formulation supports a large family of empirical loss functions and is, in specific, instantiated by exponential and linear losses. Comprehensive evaluations are conducted on several representative image benchmarks. The experiments consistently exhibit reduced computational and memory complexities of model training and deployment, without sacrificing classification accuracy.

Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby leading to severe information loss and performance degradation. In this work, we study how to achieve discrete clustering as well as reliably generalize to unseen data. We propose a novel spectral clustering scheme which deeply explores cluster label properties, including discreteness, nonnegativity, and discrimination, as well as learns robust out-of-sample prediction functions. Specifically, we explicitly enforce a discrete transformation on the intermediate continuous labels, which leads to a tractable optimization problem with a discrete solution. Besides, we preserve the natural nonnegative characteristic of the clustering labels to enhance the interpretability of the results. Moreover, to further compensate the unreliability of the learned clustering labels, we integrate an adaptive robust module with ℓ2ploss to learn prediction function for grouping unseen data. We also show that the out-of-sample component can inject discriminative knowledge into the learning of cluster labels under certain conditions. Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to several existing clustering approaches.

Many real applications in real-time news stream advertising call for efficient processing of long queries against short text. In such applications, dynamic news feeds are regarded as queries to match against an advertisement (ad) database for retrieving the k most relevant ads. The existing approaches to keyword retrieval cannot work well in this search scenario when queries are triggered at a very high frequency. To address the problem, we introduce new techniques to significantly improve search performance. First, we devise a two-level partitioning for tight upper bound estimation and a lazy evaluation scheme to delay full evaluation of unpromising candidates, which can bring three to four times performance boosting in a database with 7 million ads. Second, we propose a novel rank-aware block-oriented inverted index to further improve performance. In this index scheme, each entry in an inverted list is assigned a rank according to its importance in the ad. Then, we introduce a block-at-a-time search strategy based on the index scheme to support a much tighter upper bound estimation and a very early termination. We have conducted experiments with real datasets, and the results show that the rank-aware method can further improve performance by an order of magnitude.

Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to effectively integrate heterogeneous features from different modalities to learn hash functions using available supervising information, e.g., class labels. Existing hashing based methods generally project heterogeneous features to a common space for hash codes generation, and the supervising information is incrementally used for improving performance. However, these methods may produce ineffective hash codes, due to the failure to explore the discriminative property of supervising information and to effectively bridge the semantic gap between different modalities. To address these challenges, we propose a novel hashing based method in a linear classification framework, in which the proposed method learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective optimization algorithm is developed for the proposed method to jointly learn the modality-specific hash function, the unified binary codes and a linear classifier. Extensive experiments on three benchmark datasets highlight the advantage of the proposed method and show that it achieves the state-of-the-art performance.

We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a Hamming space. However, existing hashing methods for CF employ binary code learning procedures that most suffer from the challenging discrete constraints. Hence, those methods generally adopt a two-stage learning scheme composed of relaxed optimization via discarding the discrete constraints, followed by binary quantization. We argue that such a scheme will result in a large quantization loss, which especially compromises the performance of large-scale CF that resorts to longer binary codes. In this paper, we propose a principled CF hashing framework called Discrete Collaborative Filtering (DCF), which directly tackles the challenging discrete optimization that should have been treated adequately in hashing. The formulation of DCF has two advantages: 1) the Hamming similarity induced loss that preserves the intrinsic user-item similarity, and 2) the balanced and uncorrelated code constraints that yield compact yet informative binary codes. We devise a computationally efficient algorithm with a rigorous convergence proof of DCF. Through extensive experiments on several real-world benchmarks, we show that DCF consistently outperforms state-of-the-art CF hashing techniques, e.g, though using only 8 bits, DCF is even significantly better than other methods using 128 bits.