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…

cs.CV2026

Not All Inputs Are Valid: Towards Open-Set Video Moment Retrieval Using Language

Xiang Fang, Wanlong Fang, Daizong Liu +8

Video Moment Retrieval (VMR) targets to retrieve the specific moment corresponding to a sentence query from an untrimmed video. Although recent works have made remarkable progress…

cs.CV2026

Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language

Xiang Fang, Daizong Liu, Wanlong Fang +5

Given an untrimmed video and a sentence query, video moment retrieval using language (VMR) aims to locate a target query-relevant moment. Since the untrimmed video is overlong, alm…

cs.CV2026

CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoning

Xiang Fang, Wanlong Fang, Changshuo Wang

Multi-modal Retrieval-Augmented Generation (MMRAG) has emerged as a powerful paradigm for enhancing Multimodal Large Language Models in knowledge-intensive question answering by in…

cs.CV2026

Rethinking Video-Language Model from the Language Input Perspective

Xiang Fang, Wanlong Fang, Changshuo Wang +2

Driven by the wave of large language models, Video-Language Models (VLMs) have become a significant yet challenging technology to bridge the gap between videos and texts. Although…

cs.CV2026

Towards Unified Vision-Language Models with Incomplete Multi-Modal Inputs

Xiang Fang, Wanlong Fang, Changshuo Wang +4

Video-Language Models (VLMs) have demonstrated impressive multi-modal reasoning capabilities across diverse computer vision applications. However, these VLMs are task-specific and…

cs.CR2026

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

Xiang Fang, Wanlong Fang

Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulting in harmful or inappropriate…

cs.CV2026

Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization

Xiang Fang, Wanlong Fang, Changshuo Wang

Large Vision-Language Models (LVLMs) have transformed multi-modal understanding, excelling in tasks like image captioning and visual question answering by integrating visual and te…

cs.CV2026

Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective

Xiang Fang, Zeyu Xiong, Wanlong Fang +7

This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposal selection framework that uti…

cs.CV2024

Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network

Xiang Fang, Wanlong Fang, Changshuo Wang +5

Given some video-query pairs with untrimmed videos and sentence queries, temporal sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although previous…