CoAt-CBM improves fine-grained concept alignment in CBMs by using adaptive visual queries per concept and a contrastive loss that respects relative concept importance instead of independent BCE.
A sur- vey of the usages of deep learning for natural language pro- cessing.IEEE Transactions on Neural Networks and Learn- ing Systems, pages 604–624, 2020
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Concept-wise Attention for Fine-grained Concept Bottleneck Models
CoAt-CBM improves fine-grained concept alignment in CBMs by using adaptive visual queries per concept and a contrastive loss that respects relative concept importance instead of independent BCE.