Analyzing The Language of Visual Tokens
Paper
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2411.05001
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Published
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25
Multimodal Analysis
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Large Multi-modal Models Can Interpret Features in Large Multi-modal Models
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Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration
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On the Limitations of Vision-Language Models in Understanding Image Transforms
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Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
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When Less is Enough: Adaptive Token Reduction for Efficient Image Representation
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From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration
Paper • 2503.12821 • Published • 9 -
Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models
Paper • 2504.07951 • Published • 29 -
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models
Paper • 2505.14071 • Published • 1 -
MLLMs are Deeply Affected by Modality Bias
Paper • 2505.18657 • Published • 5 -
To Trust Or Not To Trust Your Vision-Language Model's Prediction
Paper • 2505.23745 • Published • 5 -
Vision Language Models are Biased
Paper • 2505.23941 • Published • 21 -
Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning
Paper • 2506.04755 • Published • 37 -
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better
Paper • 2506.09040 • Published • 35 -
How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks
Paper • 2507.01955 • Published • 35 -
Robust Multimodal Large Language Models Against Modality Conflict
Paper • 2507.07151 • Published • 5 -
Automating Steering for Safe Multimodal Large Language Models
Paper • 2507.13255 • Published • 3 -
When Tokens Talk Too Much: A Survey of Multimodal Long-Context Token Compression across Images, Videos, and Audios
Paper • 2507.20198 • Published • 25 -
Adapting Vision-Language Models Without Labels: A Comprehensive Survey
Paper • 2508.05547 • Published • 10 -
Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
Paper • 2508.04280 • Published • 34