D-CoDe: Scaling Image-Pretrained VLMs to Video via Dynamic Compression and Question Decomposition

• URL: http://arxiv.org/abs/2510.08818v1
• Date: Thu, 09 Oct 2025 21:08:32 GMT
• Title: D-CoDe: Scaling Image-Pretrained VLMs to Video via Dynamic Compression and Question Decomposition
• Authors: Yiyang Huang, Yizhou Wang, Yun Fu,
• Abstract summary: Video large language models (Vid-LLMs) excel in diverse video-language tasks.<n>D-CoDe is a training-free adaptation framework that incorporates dynamic compression and question decomposition.<n>Experiments demonstrate that D-CoDe effectively improves video understanding across various benchmarks.
• Score: 36.19028662042685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video large language models (Vid-LLMs), which excel in diverse video-language tasks, can be effectively constructed by adapting image-pretrained vision-language models (VLMs). However, this adaptation remains challenging, as it requires processing dense and temporally extended visual inputs that exceed the capacity of image-based models. This paper identifies the perception bottleneck and token overload as key challenges in extending image-based VLMs to the video domain. To address these issues, we propose D-CoDe, a training-free adaptation framework that incorporates dynamic compression and question decomposition. Specifically, dynamic compression alleviates the perception bottleneck through adaptive selection of representative frames and content-aware aggregation of spatial tokens, thereby reducing redundancy while preserving informative content. In parallel, question decomposition mitigates token overload by reformulating the original query into sub-questions, guiding the model to focus on distinct aspects of the video and enabling more comprehensive understanding. Experiments demonstrate that D-CoDe effectively improves video understanding across various benchmarks. Furthermore, strong performance on the challenging long-video benchmark highlights the potential of D-CoDe in handling complex video-language tasks. Code is available at https://github.com/hukcc/D-CoDe.

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