#CVPR2025 Paper Picks #1
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#CVPR2025 Paper Picks #1
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VisionZip is a compression technique that reduces number of visual tokens to improve performance AND prefill time for vision language models
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VisionZip is a compression technique that reduces number of visual tokens to improve performance AND prefill time for vision language models
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demo: 
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demo: 
https://hf.co/spaces/Senqiao/VisionZip
Senqiao/VisionZip
space
resource
https://hf.co/spaces/Senqiao/VisionZip
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paper: 
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paper: 
VisionZip: Longer is Better but Not Necessary in Vision Language Models (2412.04467)
https://hf.co/papers/2412.04467
2412.04467
paper
resource
https://hf.co/papers/2412.04467
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most of the image tokens are redundant for the LLM, so the authors ask "are all visual tokens necessary?"
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most of the image tokens are redundant for the LLM, so the authors ask "are all visual tokens necessary?"
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the method is simple:
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the method is simple:
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find which tokens have the highest attention score, merge rest of the tokens based on similarity, then merge both
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find which tokens have the highest attention score, merge rest of the tokens based on similarity, then merge both
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their method is both training-free and for fine-tuning
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their method is both training-free and for fine-tuning
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the authors report 5 point improvement on average of vision language tasks + 8x improvement in prefilling time for Llava-Next 7B and 13B 🤯
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the authors report 5 point improvement on average of vision language tasks + 8x improvement in prefilling time for Llava-Next 7B and 13B 🤯
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removing redundant tokens improve image token quality too 🥹
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removing redundant tokens improve image token quality too 🥹
#CVPR2025 Paper Picks #1
VisionZip is a compression technique that reduces number of visual tokens to improve performance AND prefill time for vision language models
demo: https://hf.co/spaces/Senqiao/VisionZip
paper: https://hf.co/papers/2412.04467
most of the image tokens are redundant for the LLM, so the authors ask "are all visual tokens necessary?"

the method is simple:
find which tokens have the highest attention score, merge rest of the tokens based on similarity, then merge both

their method is both training-free and for fine-tuning
the authors report 5 point improvement on average of vision language tasks + 8x improvement in prefilling time for Llava-Next 7B and 13B 🤯

removing redundant tokens improve image token quality too 🥹
🔥
mrdbourke John6666 BrigitteTousi linoyts DmitryRyumin