Adapting LLaMA Decoder to Vision Transformer
<|reference_start|>Adapting LLaMA Decoder to Vision Transformer: This work examines whether decoder-only Transformers such as LLaMA, which were originally designed for large language models (LLMs), can be adapted to the computer vision field. We first "LLaMAfy" a standard ViT step-by-step to align with LLaMA's architecture, and find that directly applying a causal mask to the self-attention brings an attention collapse issue, resulting in the failure to the network training. We suggest to reposition the class token behind the image tokens with a post-sequence class token technique to overcome this challenge, enabling causal self-attention to efficiently capture the entire image's information. Additionally, we develop a soft mask strategy that gradually introduces a causal mask to the self-attention at the onset of training to facilitate the optimization behavior. The tailored model, dubbed as image LLaMA (iLLaMA), is akin to LLaMA in architecture and enables direct supervised learning. Its causal self-attention boosts computational efficiency and learns complex representation by elevating attention map ranks. iLLaMA rivals the performance with its encoder-only counterparts, achieving 75.1% ImageNet top-1 accuracy with only 5.7M parameters. Scaling the model to $\sim$310M and pre-training on ImageNet-21K further enhances the accuracy to 86.0%. Extensive experiments demonstrate iLLaMA's reliable properties: shape-texture bias, calibration, quantization compatibility, ADE20K segmentation and CIFAR transfer learning. We hope our study can kindle fresh views to visual architectures in the wave of LLMs and inspire the development of unified multimodal models. Pre-trained models and codes are available https://github.com/techmonsterwang/iLLaMA.<|reference_end|>
@article{wang2024adapting,
    title={Adapting LLaMA Decoder to Vision Transformer},
    author={Jiahao Wang and Wenqi Shao and Mengzhao Chen and Chengyue Wu and Yong
  Liu and Taiqiang Wu and Kaipeng Zhang and Songyang Zhang and Kai Chen and
  Ping Luo},
    journal={arXiv preprint arXiv:2404.06773},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06773},
    primaryClass={cs.CV}
}