PuMer: Pruning and Merging Tokens for Efficient Vision Language Models (2023.acl-long)
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| Challenge: | Large-scale vision language models use Transformers to perform cross-modal interactions . state-of-the-art models are memory intensive and expensive due to quadratic complexity . |
| Approach: | They propose a token reduction framework that uses text-informed Pruning and modality-aware Merging strategies to progressively reduce the tokens of input image and text. |
| Outcome: | The proposed framework improves inference speed and memory footprint on four vision language tasks. |
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| Challenge: | Existing token reduction methods ignore image complexity and vision-language interactions, ignoring image complexity. |
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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin
| Challenge: | Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation. |
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| Challenge: | Pre-trained vision-language models have achieved impressive results in a range of vision-linguistic tasks. |
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| Challenge: | Existing methods for visual token pruning rely on predefined configurations without determining whether they achieve optimal performance. |
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| Challenge: | Existing pruning methods for large vision language models use visual tokens to prune . existing methods fail to balance efficiency and semantic alignment due to large number of visual token. |
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| Challenge: | Efficient inference in Large Vision Language Models is constrained by the high cost of processing thousands of visual tokens. |
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| Challenge: | Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts. |
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AdaV: Adaptive Text-visual Redirection for Vision-Language Models (2025.findings-acl)
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| Challenge: | Vision-language models often generate excessive visual tokens, leading to poor performance . a novel training-free visual token pruning method is proposed to improve performance despite the computational cost associated with VLMs. |
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