9 papers
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…
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…
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…
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…
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…
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…
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…
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…
Taylor Series-Inspired Local Structure Fitting Network for Few-shot Point Cloud Semantic Segmentation
Changshuo Wang, Shuting He, Xiang Fang +3
Few-shot point cloud semantic segmentation aims to accurately segment "unseen" new categories in point cloud scenes using limited labeled data. However, pretraining-based methods n…