collaborators

15 papers

cs.CV2026

Turing Patterns for Multimedia: Reaction-Diffusion Multi-Modal Fusion for Language-Guided Video Moment Retrieval

Xiang Fang, Wanlong Fang, Wei Ji +1

Video-language models are pivotal for tasks such as moment retrieval and highlight detection, yet they often struggle to capture the dynamic, non-linear interactions between tempor…

cs.RO2026

Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation

Xiang Fang, Wanlong Fang, Changshuo Wang

Vision-Language Navigation in Continuous Environments (VLN-CE) poses a formidable challenge for autonomous agents, requiring seamless integration of natural language instructions a…

cs.AI2026

Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition

Wanlong Fang, Tianle Zhang, Wen Tao +1

Understanding how multimodal large language models use different modalities is important for reliable reasoning. We employ Partial Information Decomposition (PID) as a decision-lev…

cs.CV2026

SLAP: The Semantic Least Action Principle for Variational Video-Language Modeling

Xiang Fang, Wanlong Fang

In the era of Large Video-Language Models (LVLMs), the computational necessity of sparse frame sampling creates a fundamental ``temporal gap'', rendering models blind to critical c…

cs.CV2026

Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness

Xiang Fang, Wanlong Fang, Wei Ji

Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. However, this semantic abstracti…

cs.CV2026

Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding

Xiang Fang, Daizong Liu, Wanlong Fang +4

This paper addresses the task of temporal sentence grounding (TSG). Although many respectable works have made decent achievements in this important topic, they severely rely on mas…