FOLDER: Accelerating Multi-Modal Large Language Models with Enhanced Performance

Haicheng Wang, Zhemeng Yu, Gabriele Spadaro, Chen Ju, Victor Quétu, Shuai Xiao, Enzo Tartaglione; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 23614-23625

Recently, Multi-modal Large Language Models (MLLMs) have shown remarkable effectiveness for multi-modal tasks due to their ability of cross-modal understanding. However, processing long sequences of visual tokens extracted from visual backbones poses challenges for deployment in real-time applications. To address this issue, we introduce FOLDER, a simple yet effective plug-and-play module designed to reduce the length of the visual token sequence, mitigating computational and memory demands during both training and inference. Through a comprehensive analysis of the token reduction process in the vision encoder, we analyze the information loss introduced by different reduction strategies and develop FOLDER to preserve key information while removing visual redundancy. We show the effectiveness of FOLDER by integrating it into the visual backbone of various MLLMs, significantly accelerating the inference phase. Furthermore, we evaluate its utility as a training accelerator or even performance booster for MLLMs. FOLDER achieves comparable or even better performance than the original models, while dramatically reducing complexity by removing up to 70% of visual tokens. Our code is available at https://github.com/anakin-skywalker-Joseph/Folder.