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In this paper, we introduce PruneVid, a visual token pruning method designed to enhance the efficiency of multi-modal video understanding.
Study on density peaks clustering based on k-nearest neighbors and principal component analysis
Mingjing Du, Shifei Ding, and Hongjie Jia · 2016
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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2019
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Dynamicvit: Efficient vision transformers with dynamic token sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
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Actionclip: A new paradigm for video action recognition
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Spvit: Enabling faster vision transformers via latency-aware soft token pruning
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Video-llava: Learning united visual representation by alignment before projection
Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan · 2023
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Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2023
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An image grid can be worth a video: Zero-shot video question answering using a vlm
Wonkyun Kim, Changin Choi, Wonseok Lee, and Wonjong Rhee · 2024
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Freeva: Offline mllm as training-free video assistant
Wenhao Wu · 2024
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Qwen2 technical report
An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al · 2024
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Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis
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Videochat: Chat-centric video understanding
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Unmasked teacher: Towards training-efficient video foundation models
Kunchang Li, Yali Wang, Yizhuo Li, Yi Wang, Yinan He, Limin Wang, and Yu Qiao
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Mvbench: A comprehensive multi-modal video understanding benchmark
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An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models
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