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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…
Guiding the Inner Eye: A Framework for Hierarchical and Flexible Visual Grounded Reasoning
Zhaoyang Wei, Wenchao Ding, Yanchao Hao +1
Models capable of "thinking with images" by dynamically grounding their reasoning in visual evidence represent a major leap in multimodal AI. However, replicating and advancing thi…
VER-Bench: Evaluating MLLMs on Reasoning with Fine-Grained Visual Evidence
Chenhui Qiang, Zhaoyang Wei, Xumeng Han +5
With the rapid development of MLLMs, evaluating their visual capabilities has become increasingly crucial. Current benchmarks primarily fall into two main types: basic perception b…
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…
Semantic-aware SAM for Point-Prompted Instance Segmentation
Zhaoyang Wei, Pengfei Chen, Xuehui Yu +3
Single-point annotation in visual tasks, with the goal of minimizing labelling costs, is becoming increasingly prominent in research. Recently, visual foundation models, such as Se…