10 papers
-VLA: Boosting Vision-Language Models for Generalizable Manipulation via Layer Mixture and Meta-Skills
Siyao Xiao, Yuhong Zhang, Zhifang Liu +9
Current Vision-Language-Action (VLA) models predominantly rely on end-to-end fine-tuning. While effective, this paradigm compromises the inherent generalization capabilities of Vis…
ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering
ZhengXian Wu, Hangrui Xu, Kai Shi +8
Knowledge-based Visual Question Answering (KB-VQA) requires models to combine image understanding with external knowledge. Most prior methods use a fixed retrieve-then-generate pip…
R3G: A Reasoning-Retrieval-Reranking Framework for Vision-Centric Answer Generation
Zhuohong Chen, Zhengxian Wu, Zirui Liao +6
Vision-centric retrieval for VQA requires retrieving images to supply missing visual cues and integrating them into the reasoning process. However, selecting the right images and i…
Learning to Search: A Decision-Based Agent for Knowledge-Based Visual Question Answering
Zhuohong Chen, Zhenxian Wu, Yunyao Yu +6
Knowledge-based visual question answering (KB-VQA) requires vision-language models to understand images and use external knowledge, especially for rare entities and long-tail facts…
Stabilizing Unsupervised Self-Evolution of MLLMs via Continuous Softened Retracing reSampling
Yunyao Yu, Zhengxian Wu, Zhuohong Chen +6
In the unsupervised self-evolution of Multimodal Large Language Models, the quality of feedback signals during post-training is pivotal for stable and effective learning. However,…
When Models Judge Themselves: Unsupervised Self-Evolution for Multimodal Reasoning
Zhengxian Wu, Kai Shi, Chuanrui Zhang +10
Recent progress in multimodal large language models has led to strong performance on reasoning tasks, but these improvements largely rely on high-quality annotated data or teacher-…