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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…
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…
DREAM: Document Reconstruction via End-to-end Autoregressive Model
Xin Li, Mingming Gong, Yunfei Wu +7
Document reconstruction constitutes a significant facet of document analysis and recognition, a field that has been progressively accruing interest within the scholarly community.…
TACO: Think-Answer Consistency for Optimized Long-Chain Reasoning and Efficient Data Learning via Reinforcement Learning in LVLMs
Zhehan Kan, Yanlin Liu, Kun Yin +8
DeepSeek R1 has significantly advanced complex reasoning for large language models (LLMs). While recent methods have attempted to replicate R1's reasoning capabilities in multimoda…
Break the Visual Perception: Adversarial Attacks Targeting Encoded Visual Tokens of Large Vision-Language Models
Yubo Wang, Chaohu Liu, Yanqiu Qu +3
Large vision-language models (LVLMs) integrate visual information into large language models, showcasing remarkable multi-modal conversational capabilities. However, the visual mod…
HRVDA: High-Resolution Visual Document Assistant
Chaohu Liu, Kun Yin, Haoyu Cao +6
Leveraging vast training data, multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities and achieved remarkable performance a…
Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models
Xin Li, Yunfei Wu, Xinghua Jiang +6
Recently, the advent of Large Visual-Language Models (LVLMs) has received increasing attention across various domains, particularly in the field of visual document understanding (V…