activity
20232026
most citedUnsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering

21 citations · 27 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Test-Time Perturbation Learning with Delayed Feedback for Vision-Language-Action Models

Zehua Zang, Xi Wang, Fuchun Sun +4

Vision-Language-Action models (VLAs) achieve remarkable performance in sequential decision-making but remain fragile to subtle environmental shifts, such as small changes in object…

cs.CV2026

RS-SSM: Refining Forgotten Specifics in State Space Model for Video Semantic Segmentation

Kai Zhu, Zhenyu Cui, Zehua Zang +1

Recently, state space models have demonstrated efficient video segmentation through linear-complexity state space compression. However, Video Semantic Segmentation (VSS) requires p…

cs.MA2024★ 2 cited

M2I2: Learning Efficient Multi-Agent Communication via Masked State Modeling and Intention Inference

Chuxiong Sun, Peng He, Qirui Ji +4

Communication is essential in coordinating the behaviors of multiple agents. However, existing methods primarily emphasize content, timing, and partners for information sharing, of…

cs.AI2024★ 2 cited

Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective

Jiangmeng Li, Zehua Zang, Qirui Ji +6

Representations learned by self-supervised approaches are generally considered to possess sufficient generalizability and discriminability. However, we disclose a nontrivial mutual…

cs.MA2024★ 2 cited

T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration

Chuxiong Sun, Zehua Zang, Jiabao Li +4

Communication stands as a potent mechanism to harmonize the behaviors of multiple agents. However, existing works primarily concentrate on broadcast communication, which not only l…

cs.SI2023★ 21 cited

Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering

Yuanyuan Guo, Zehua Zang, Hang Gao +4

Detecting events from social media data streams is gradually attracting researchers. The innate challenge for detecting events is to extract discriminative information from social…