1 citations · 2 across the 9 of their papers we have counts for
7 papers · 1 filter
Video-HolmesV2: Can MLLMs Reason with Spatio-Temporal Audio-Visual Evidence in Long Videos?
Zhaoyang Wei, Zipeng Wang, Yushe Cao +10
Multimodal Large Language Models have demonstrated impressive video understanding, yet their ability to reason over long-form narratives is often masked by visual-centric evaluatio…
Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes
Zhaoyang Wei, Bowen Jiang, Xumeng Han +6
While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes unde…
LUT: Latent Utility Training for Visual Reasoning
Jiaxuan Kang, Siyu Chen, Mingda Li +6
Multimodal large language models have advanced visual understanding, yet perception-intensive reasoning remains challenging. Recent latent visual reasoning methods introduce hidden…
HeroGS: Hierarchical Guidance for Robust 3D Gaussian Splatting under Sparse Views
Jiashu Li, Xumeng Han, Zhaoyang Wei +5
3D Gaussian Splatting (3DGS) has recently emerged as a promising approach in novel view synthesis, combining photorealistic rendering with real-time efficiency. However, its succes…
What Makes VLMs Robust? Towards Reconciling Robustness and Accuracy in Vision-Language Models
Sen Nie, Jie Zhang, Zhongqi Wang +3
Achieving adversarial robustness in Vision-Language Models (VLMs) inevitably compromises accuracy on clean data, presenting a long-standing and challenging trade-off. In this work,…
SAPNet++: Evolving Point-Prompted Instance Segmentation with Semantic and Spatial Awareness
Zhaoyang Wei, Xumeng Han, Xuehui Yu +4
Single-point annotation is increasingly prominent in visual tasks for labeling cost reduction. However, it challenges tasks requiring high precision, such as the point-prompted ins…
Video Evidence to Reasoning Efficient Video Understanding via Explicit Evidence Grounding
Yanxiang Huang, Guohua Gao, Zhaoyang Wei
Large Vision-Language Models (LVLMs) face a fundamental dilemma in video reasoning: they are caught between the prohibitive computational costs of verbose reasoning and the halluci…