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cs.CV2025
Mitigating Object Hallucination via Robust Local Perception Search
Zixian Gao, Chao Yang, Zhanhui Zhou +2
Recent advancements in Multimodal Large Language Models (MLLMs) have enabled them to effectively integrate vision and language, addressing a variety of downstream tasks. However, d…
cs.CV2025
VLMs Can Aggregate Scattered Training Patches
Zhanhui Zhou, Lingjie Chen, Chao Yang +1
One way to mitigate risks in vision-language models (VLMs) is to remove dangerous samples in their training data. However, such data moderation can be easily bypassed when harmful…
cs.CV2025
IDMR: Towards Instance-Driven Precise Visual Correspondence in Multimodal Retrieval
Bangwei Liu, Yicheng Bao, Shaohui Lin +5
Multimodal retrieval systems are becoming increasingly vital for cutting-edge AI technologies, such as embodied AI and AI-driven digital content industries. However, current multim…