CA-Stream: Attention-based pooling for interpretable image recognition

Autor:	Torres, Felipe, Zhang, Hanwei, Sicre, Ronan, Ayache, Stéphane, Avrithis, Yannis
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: CVPR XAI4CV workshop 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.14996 Zobrazit plný text záznamu View this record from Arxiv