Human parsing requires fine-grained, pixel-level delineation of human parts and accessories, yet visually correlated categories and long-tailed parts often cause semantic confusion and boundary ambiguity. We propose PSRNet, a Progressive Semantic Refinement Network that exploits fixed class semantic embeddings derived from category text to refine parsing in a coarse-to-fine manner. First, a Text-Conditioned Feature Modulation (TCFM) module injects class semantics to modulate encoder features, enhancing low-level discriminability for confusing parts. Second, a Semantic-Embedding Calibration and Fusion (SECF) module combines a conventional linear classifier with an embedding-similarity head to calibrate category logits, effectively reducing misclassification among semantically close classes. Third, we introduce a Morphology-Guided Boundary Refinement loss (MGBR) that constructs boundary supervision via dilation–erosion operations on predictions and ground truth, encouraging sharper and more consistent part boundaries. Extensive experiments on two widely used benchmarks, LIP and CIHP, demonstrate that PSRNet consistently improves both parsing accuracy and boundary quality over strong baselines, with particularly notable gains on confusing part pairs.

Efficiently editing the knowledge of large language models (LLMs) is crucial for real-world adaptation and error correction, yet remains a fundamental challenge. Existing approaches, primarily based on the Locate-Then-Edit paradigm, have achieved promising results by first identifying key parameters and then updating them; however, they still suffer from catastrophic over-editing, where sequential updates degrade both preserved and edited knowledge, especially as batch sizes grow. To address this, we propose Important Weight Constrained Knowledge Editing (IWCKEdit), a simple yet effective framework that introduces weight protection into the model editing process. IWCKEdit comprises two key modules: a front-end Protected Weight Identification Module that leverages Fisher information to detect weights essential for knowledge retention and effective editing, and a back-end Constrained Weight Updating Module that safeguards these critical weights during targeted updates for knowledge editing. Extensive experiments on large-scale models including GPT2-XL, Llama3-8B, and Qwen2.5-7B demonstrate that IWCKEdit achieves superior knowledge editing accuracy while robustly preserving both newly introduced and existing knowledge, substantially advancing the state of the art in LLM editing.

Large web-scale datasets have driven the rapid advancement of pre-trained language models (PLMs), but unauthorized data usage has raised serious copyright concerns. Existing dataset ownership verification (DOV) methods typically assume that watermarks remain stable during inference; however, this assumption often fails under natural noise and adversary-crafted perturbations. We propose the first certified dataset ownership verification method for PLMs under a gray-box setting (i.e., the defender can only query the suspicious model but is aware of its input representation module), based on dual-space smoothing (i.e., DSSmoothing). To address the challenges of text discreteness and semantic sensitivity, DSSmoothing introduces continuous perturbations in the embedding space to capture semantic robustness and applies controlled token reordering in the permutation space to capture sequential robustness. DSSmoothing consists of two stages: in the first stage, triggers are collaboratively embedded in both spaces to generate norm-constrained and robust watermarked datasets; in the second stage, randomized smoothing is applied in both spaces during verification to compute the watermark robustness (WR) of suspicious models and statistically compare it with the principal probability (PP) values of a set of benign models. Theoretically, DSSmoothing provides provable robustness guarantees for dataset ownership verification by ensuring that WR consistently exceeds PP under bounded dual-space perturbations. Extensive experiments on multiple representative web datasets demonstrate that DSSmoothing achieves stable and reliable verification performance and exhibits robustness against potential adaptive attacks. Our code is available at https://github.com/NcepuQiaoTing/DSSmoothing.

Temporal interactions form the crux of numerous real-world scenarios, thus necessitating effective modeling in temporal graph representation learning. Despite extensive research within this domain, we identify a significant oversight in current methodologies: the temporal-spatial dynamics in graphs, encompassing both structural and temporal coherence, remain largely unaddressed. In an effort to bridge this research gap, we present a novel framework termed Graph Representation learning enhanced by Periodic and Community Interactions (GRPCI). GRPCI consists of two primary mechanisms devised explicitly to tackle the aforementioned challenge. Firstly, to utilize latent temporal dynamics, we propose a novel periodicity-based neighborhood aggregation mechanism that underscores neighbors engaged in a periodic interaction pattern. This mechanism seamlessly integrates the element of periodicity into the model. Secondly, to exploit structural dynamics, we design a novel contrastive-based local community representation learning mechanism. This mechanism features a heuristic dynamic contrastive pair sampling strategy aimed at enhancing the modeling of the latent distribution of local com munities within the graphs. Through the incorporation of these two mechanisms, GRPCI markedly augments the performance of graph networks. Empirical evaluations, conducted via a temporal link prediction task across five real-life datasets, attest to the superior performance of GRPCI in comparison to existing state of-the-art methodologies. The results of this study validate the efficacy of GRPCI, thereby establishing a new benchmark for future research in the field of temporal graph representation learning. Our findings underscore the importance of considering both temporal and structural consistency in temporal graph learning, and advocate for further exploration of this paradigm.

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.

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.