The surge in online interactions and advancement in Big Data related techniques have generated vast amounts of hybrid data in the sense that the data are symbolic, numerical or missing features, and usually only a small number of data objects possess true labels due to high annotation costs. A necessary step of fully releasing the potential of these partially labeled hybrid data lies in feature selection, for which the neighborhood rough set (NRS) is an efficient mathematical method to apply. In NRS, setting proper neighborhood granules greatly influences the effectiveness and robustness of algorithms atop it. However, existing methods usually determine the optimal neighborhood radius of neighborhood granule via computationally intensive grid search, where the neighborhood radius for each object is the same, i.e., “unadaptive”. Some methods investigate adaptive granulation strategies, yet they inevitably hinge on preset parameters or a-prior knowledge. To tackle this problem, we propose an adaptive granules-enabled semi-supervised feature selection method that can adaptively generate suitable neighborhood radii for both labeled and unlabeled objects. The core idea lies in using the purity of decision labels as the threshold for granularity maximization construction. Then, by combining with neighborhood entropy and local density, a feature metric is designed to measure the feature significance. A semi-supervised feature selection algorithm is utilized to select feature subset by using the information from both labeled and unlabeled objects. Instead of hinging on expert knowledge, the proposed method only rely on the data per se. Experimental results on real-world datasets demonstrate the effectiveness of the designed method and its superiority over other state-of-the-art.

Microservice architecture has demonstrated immense advantages in solving the scalability and maintainability problems confronted by traditional monolithic systems. However, existing microservice architectures still encounter the problem of blurred boundaries, which significantly impact the system performance. The primary reason being the lack of unified evaluation indicators and efficient microservice splitting strategies. Specifically, some approaches divide entities like classes subjectively, resulting in inconsistent microservice boundaries, while others utilize objective criteria, but their complexity makes practical implementation challenging. To this end, we formalize the optimal microservice granulation problem, show its NPcompleteness and propose the granulation solution based on granular-ball computing. Our designs encompass a microservicebased information decision system to quantify the structural similarity between entities, a microservice granulation representation method based on granular-ball computing, and an optimal microservice granulation method given fixed budget constraint. Extensive experiments are conducted on open-source microservice projects to showcase the granulation result and the experimental results also show the efficacy and effectiveness.

Self-supervised learning (SSL) has demonstrated remarkable success in 3D point cloud analysis, particularly through masked autoencoders (MAEs). However, existing MAE-based methods lack rotation invariance, leading to significant performance degradation when processing arbitrarily rotated point clouds in real-world scenarios. To address this limitation, we introduce Handcrafted Feature-Based Rotation-Invariant Masked Autoencoder (HFBRI-MAE), a novel framework that refines the MAE design with rotation-invariant handcrafted features to ensure stable feature learning across different orientations. By leveraging both rotation-invariant local and global features for token embedding and position embedding, HFBRI-MAE effectively eliminates rotational dependencies while preserving rich geometric structures. Additionally, we redefine the reconstruction target to a canonically aligned version of the input, mitigating rotational ambiguities. Extensive experiments on ModelNet40, ScanObjectNN, and ShapeNetPart demonstrate that HFBRI-MAE consistently outperforms existing methods in object classification, segmentation, and few-shot learning, highlighting its robustness and strong generalization ability in real-world 3D applications.

Recent advances in zero-shot text-to-3D human generation, which employ the human model prior (e.g., SMPL) or Score Distillation Sampling (SDS) with pre-trained text-to-image diffusion models, have been groundbreaking. However, SDS may provide inaccurate gradient directions under the weak diffusion guidance, as it tends to produce over-smoothed results and generate body textures that are inconsistent with the detailed mesh geometry. Therefore, directly leveraging existing strategies for high-fidelity text-to-3D human texturing is challenging. In this work, we propose a model called PaintHuman to addresses the challenges from two perspectives. We first propose a novel score function, Denoised Score Distillation (DSD), which directly modifies the SDS by introducing negative gradient components to iteratively correct the gradient direction and generate high-quality textures. In addition, we use the depth map as a geometric guide to ensure that the texture is semantically aligned to human mesh surfaces. To guarantee the quality of rendered results, we employ geometry-aware networks to predict surface materials and render realistic human textures. Extensive experiments, benchmarked against state-of-the-art (SoTA) methods, validate the efficacy of our approach.Project page: https://painthuman.github.io/.

Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of learning frameworks for invariance have seldom been looked into. In this work, we review rotation invariance (RI) in terms of point cloud registration (PCR) and propose an effective framework for rotation invariance learning via three sequential stages, namely rotation-invariant shape encoding, aligned feature integration, and deep feature registration. We first encode shape descriptors constructed with respect to reference frames defined over different scales, e.g., local patches and global topology, to generate rotation-invariant latent shape codes. Within the integration stage, we propose an Aligned Integration Transformer (AIT) to produce a discriminative feature representation by integrating point-wise self- and cross-relations established within the shape codes. Meanwhile, we adopt rigid transformations between reference frames to align the shape codes for feature consistency across different scales. Finally, the deep integrated feature is registered to both rotation-invariant shape codes to maximize their feature similarities, such that rotation invariance of the integrated feature is preserved and shared semantic information is implicitly extracted from shape codes. Experimental results on 3D shape classification, part segmentation, and retrieval tasks prove the feasibility of our framework. Our project page is released at: https://rotation3d.github.io/.

Recent interest in point cloud analysis has led rapid progress in designing deep learning methods for 3D models. However, state-of-the-art models are not robust to rotations, which remains an unknown prior to real applications and harms the model performance. In this work, we introduce a novel Patch-wise Rotation-invariant network (PaRot), which achieves rotation invariance via feature disentanglement and produces consistent predictions for samples with arbitrary rotations. Specifically, we design a siamese training module which disentangles rotation invariance and equivariance from patches defined over different scales, e.g., the local geometry and global shape, via a pair of rotations. However, our disentangled invariant feature loses the intrinsic pose information of each patch. To solve this problem, we propose a rotation-invariant geometric relation to restore the relative pose with equivariant information for patches defined over different scales. Utilising the pose information, we propose a hierarchical module which implements intra-scale and inter-scale feature aggregation for 3D shape learning. Moreover, we introduce a pose-aware feature propagation process with the rotation-invariant relative pose information embedded. Experiments show that our disentanglement module extracts high-quality rotation-robust features and the proposed lightweight model achieves competitive results in rotated 3D object classification and part segmentation tasks.

Text-driven generation models are flourishing in video generation and editing. However, face-centric text-to-video generation remains a challenge due to the lack of a suitable dataset containing high-quality videos and highly relevant texts. This paper presents Celeb V- Text, a large-scale, di-verse, and high-quality dataset of facial text-video pairs, to facilitate research on facial text-to- video generation tasks. CelebV-Text comprises 70,000 in-the-wild face video clips with diverse visual content, each paired with 20 texts gen-erated using the proposed semi-automatic text generation strategy. The provided texts are of high quality, describing both static and dynamic attributes precisely. The supe-riority of CelebV- Text over other datasets is demonstrated via comprehensive statistical analysis of the videos, texts, and text-video relevance. The effectiveness and potential of CelebV- Text are further shown through extensive self-evaluation. A benchmark is constructed with representative methods to standardize the evaluation of the facial text-to-video generation task. All data and models are publicly available11Project page: https://celebv-text.github.io.

Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding. Apart from coarse semantic class prediction and bounding box regression as in traditional 3D object detection, 3D dense captioning aims at producing a further and finer instance-level label of natural language description on visual appearance and spatial relations for each scene object of interest. To detect and describe objects in a scene, following the spirit of neural machine translation, we propose a transformer-based encoder-decoder architecture, namely SpaCap3D, to transform objects into descriptions, where we especially investigate the relative spatiality of objects in 3D scenes and design a spatiality-guided encoder via a token-to-token spatial relation learning objective and an object-centric decoder for precise and spatiality-enhanced object caption generation. Evaluated on two benchmark datasets, ScanRefer and ReferIt3D, our proposed SpaCap3D outperforms the baseline method Scan2Cap by 4.94% and 9.61% in [email protected], respectively. Our project page with source code and supplementary files is available at https://SpaCap3D.github.io/.

Feature selection can reduce the dimensionality of data effectively. Most of the existing feature selection approaches using rough sets focus on the static single type data. However, in many real-world applications, data sets are the hybrid data including symbolic, numerical and missing features. Meanwhile, an object set in the hybrid data often changes dynamically with time. For the hybrid data, since acquiring all the decision labels of them is expensive and time-consuming, only small portion of the decision labels for the hybrid data is obtained. Therefore, in this paper, incremental feature selection algorithms based on information granularity are developed for dynamic partially labeled hybrid data with the variation of an object set. At first, the information granularity is given to measure the feature significance for partially labeled hybrid data. Then, incremental mechanisms of information granularity are proposed with the variation of an object set. On this basis, incremental feature selection algorithms with the variation of a single object and group of objects are proposed, respectively. Finally, extensive experimental results on different UCI data sets demonstrate that compared with the non-incremental feature selection algorithms, incremental feature selection algorithms can select a subset of features in shorter time without losing the classification accuracy, especially when the group of objects changes dynamically, the group incremental feature selection algorithm is more efficient.

Scene understanding is a critical problem in computer vision. In this paper, we propose a 3D point-based scene graph generation (SGGpoint) framework to effectively bridge perception and reasoning to achieve scene under-standing via three sequential stages, namely scene graph construction, reasoning, and inference. Within the reasoning stage, an EDGE-oriented Graph Convolutional Network (EdgeGCN) is created to exploit multi-dimensional edge features for explicit relationship modeling, together with the exploration of two associated twinning interaction mechanisms between nodes and edges for the independent evolution of scene graph representations. Overall, our integrated SGGpointframework is established to seek and infer scene structures of interest from both real-world and synthetic 3D point-based scenes. Our experimental results show promising edge-oriented reasoning effects on scene graph generation studies. We also demonstrate our method advantage on several traditional graph representation learning benchmark datasets, including the node-wise classification on citation networks and whole-graph recognition problems for molecular analysis.

Discrete point cloud objects lack sufficient shape descriptors of 3D geometries. In this paper, we present a novel method for aggregating hypothetical curves in point clouds. Sequences of connected points (curves) are initially grouped by taking guided walks in the point clouds, and then subsequently aggregated back to augment their pointwise features. We provide an effective implementation of the proposed aggregation strategy including a novel curve grouping operator followed by a curve aggregation operator. Our method was benchmarked on several point cloud analysis tasks where we achieved the state-of-the-art classification accuracy of 94.2% on the ModelNet40 classification task, instance IoU of 86.8% on the ShapeNetPart segmentation task and cosine error of 0.11 on the ModelNet40 normal estimation task. Our project page with source code is available at: https://curvenet.github.io/.

Micro-expressions are reflections of people's true feelings and motives, which attract an increasing number of researchers into the study of automatic facial micro-expression recognition. The short detection window, the subtle facial muscle movements, and the limited training samples make micro-expression recognition challenging. To this end, we propose a novel Identity-aware and Capsule-Enhanced Generative Adversarial Network with graph-based reasoning (ICE-GAN), introducing micro-expression synthesis as an auxiliary task to assist recognition. The generator produces synthetic faces with controllable micro-expressions and identity-aware features, whose long-ranged dependencies are captured through the graph reasoning module (GRM), and the discriminator detects the image authenticity and expression classes. Our ICE-GAN was evaluated on Micro-Expression Grand Challenge 2019 (MEGC2019) with a significant improvement (12.9%) over the winner and surpassed other state-of-the-art methods.

Destination prediction is an essential task for a number of emerging location based applications such as recommending sightseeing places and sending targeted advertisements. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, existing techniques suffer from the "data sparsity problem", i.e., the number of available historical trajectories is far from sufficient to cover all possible trajectories. This problem considerably limits the amount of query trajectories whose predicted destinations can be inferred. In this demonstration, we showcase a system named "DesTeller" that is interactive, user-friendly, publicly accessible, and capable of answering real-time queries. The underlying algorithm Sub-Trajectory Synthesis (SubSyn) successfully addressed the data sparsity problem and is able to predict destinations for almost every query submitted by travellers. We also consider the privacy protection issue in case an adversary uses SubSyn algorithm to derive sensitive location information of users.