CA-Stream: Attention-based pooling for interpretable image recognition
<|reference_start|>CA-Stream: Attention-based pooling for interpretable image recognition: Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map. Additionally, attention-based pooling serves as a form of masking the in feature space. Motivated by this observation, we design an attention-based pooling mechanism intended to replace Global Average Pooling (GAP) at inference. This mechanism, called Cross-Attention Stream (CA-Stream), comprises a stream of cross attention blocks interacting with features at different network depths. CA-Stream enhances interpretability in models, while preserving recognition performance.<|reference_end|>
@article{torres2024ca-stream:,
    title={CA-Stream: Attention-based pooling for interpretable image recognition},
    author={Felipe Torres, Hanwei Zhang, Ronan Sicre, St'ephane Ayache, Yannis
  Avrithis},
    journal={arXiv preprint arXiv:2404.14996},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14996},
    primaryClass={cs.CV}
}