@article{yu2023mix,
  title={Mix-ViT: Mixing attentive vision transformer for ultra-fine-grained visual categorization},
  author={Yu, Xiaohan and Wang, Jun and Zhao, Yang and Gao, Yongsheng},
  journal={Pattern Recognition},
  volume={135},
  pages={109131},
  year={2023},
  publisher={Elsevier}
}

Ultra-fine-grained visual categorization (ultra-FGVC) moves down the taxonomy level to classify sub-granularity categories of fine-grained objects. This inevitably poses a challenge, i.e., classifying highly similar objects with limited samples, which impedes the performance of recent advanced vision transformer methods. To that end, this paper introduces Mix-ViT, a novel mixing attentive vision transformer to address the above challenge towards improved ultra-FGVC. The core design is a self-supervised module that mixes the high-level sample tokens and learns to predict whether a token has been substituted after attentively substituting tokens. This drives the model to understand the contextual discriminative details among inter-class samples. Via incorporating such a self-supervised module, the network gains more knowledge from the intrinsic structure of input data and thus improves generalization capability with limited training sample. The proposed Mix-ViT achieves competitive performance on seven publicly available datasets, demonstrating the potential of vision transformer compared to CNN for the first time in addressing the challenging ultra-FGVC tasks.

wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz

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