The rapid success of Vision Large Language Models (VLLMs) often depends on the high-resolution images with abundant visual tokens, which hinders training and deployment efficiency. Current training-free visual token compression methods exhibit serious performance degradation in tasks involving high-resolution, text-oriented image understanding and reasoning. In this paper, we propose an efficient visual token compression framework for text-oriented VLLMs in high-resolution scenarios. In particular, we employ a light-weight self-distillation pre-training stage to compress the visual tokens, requiring a limited numbers of image-text pairs and minimal learnable parameters. Afterwards, to mitigate potential performance degradation of token-compressed models, we construct a high-quality post-train stage. To validate the effectiveness of our method, we apply it to an advanced VLLMs, InternVL2. Experimental results show that our approach significantly reduces computational overhead while outperforming the baselines across a range of text-oriented benchmarks. We will release the models and code soon.
@article{arxiv.2502.18512,
title = {FCoT-VL:Advancing Text-oriented Large Vision-Language Models with Efficient Visual Token Compression},
author = {Jianjian Li and Junquan Fan and Feng Tang and Gang Huang and Shitao Zhu and Songlin Liu and Nian Xie and Wulong Liu and Yong Liao},
journal= {arXiv preprint arXiv:2502.18512},
year = {2025}
}