FlexAttention for Efficient High-Resolution Vision-Language Models
<|reference_start|>FlexAttention for Efficient High-Resolution Vision-Language Models: Current high-resolution vision-language models encode images as high-resolution image tokens and exhaustively take all these tokens to compute attention, which significantly increases the computational cost. To address this problem, we propose FlexAttention, a flexible attention mechanism for efficient high-resolution vision-language models. Specifically, a high-resolution image is encoded both as high-resolution tokens and low-resolution tokens, where only the low-resolution tokens and a few selected high-resolution tokens are utilized to calculate the attention map, which greatly shrinks the computational cost. The high-resolution tokens are selected via a high-resolution selection module which could retrieve tokens of relevant regions based on an input attention map. The selected high-resolution tokens are then concatenated to the low-resolution tokens and text tokens, and input to a hierarchical self-attention layer which produces an attention map that could be used for the next-step high-resolution token selection. The hierarchical self-attention process and high-resolution token selection process are performed iteratively for each attention layer. Experiments on multimodal benchmarks prove that our FlexAttention outperforms existing high-resolution VLMs (e.g., relatively ~9% in V* Bench, ~7% in TextVQA), while also significantly reducing the computational cost by nearly 40%.<|reference_end|>
@article{li2024flexattention,
    title={FlexAttention for Efficient High-Resolution Vision-Language Models},
    author={Junyan Li, Delin Chen, Tianle Cai, Peihao Chen, Yining Hong, Zhenfang
  Chen, Yikang Shen, and Chuang Gan},
    journal={arXiv preprint arXiv:2407.20228},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20228},
    primaryClass={cs.CV}
}