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LOCUS: Local Visual Cue Search for Enhancing Fine-Grained Perception in Multimodal Large Language Models
Zhou Tao, Fang Zhang, Zewen Ding +5
Multimodal Large Language Models (MLLMs) remain unreliable on fine-grained visual perception, even when high-resolution inputs preserve the necessary local details. We identify thi…
Dynamic Token Compression for Efficient Video Understanding through Reinforcement Learning
Shida Wang, YongXiang Hua, Zhou Tao +2
Multimodal Large Language Models have demonstrated remarkable capabilities in video understanding, yet face prohibitive computational costs and performance degradation from ''conte…
When Thinking Hurts: Mitigating Visual Forgetting in Video Reasoning via Frame Repetition
Xiaokun Sun, Yubo Wang, Haoyu Cao +1
Recently, Multimodal Large Language Models (MLLMs) have demonstrated significant potential in complex visual tasks through the integration of Chain-of-Thought (CoT) reasoning. Howe…
DiG: Differential Grounding for Enhancing Fine-Grained Perception in Multimodal Large Language Model
Zhou Tao, Shida Wang, Yongxiang Hua +2
Multimodal Large Language Models have achieved impressive performance on a variety of vision-language tasks, yet their fine-grained visual perception and precise spatial reasoning…
BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models
Jianting Tang, Yubo Wang, Haoyu Cao +1
Mainstream Multimodal Large Language Models (MLLMs) achieve visual understanding by using a vision projector to bridge well-pretrained vision encoders and large language models (LL…
AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization
Chaohu Liu, Tianyi Gui, Yu Liu +1
Large Vision-Language Models (LVLMs), such as GPT-4o and LLaVA, have recently witnessed remarkable advancements and are increasingly being deployed in real-world applications. Howe…