CAMAL: Improving Attention Alignment and Faithfulness with Segmentation Masks
Many vision datasets now provide segmentation masks in addition to annotated images to support a wide range of tasks. In this work, we propose Class Activation Map Attention Learning (CAMAL), an efficient and scalable method that utilizes segmentation masks to improve attention alignment and faithfulness in vision models. Specifically, attention alignment refers to the degree to which a model's attention aligns with ground-truth discriminative regions, while attention faithfulness refers to the degree to which a model's attention influences its decision. Improving both attention alignment and faithfulness is essential for ensuring that model attention is both spatially accurate and causal...
| Authors | Rajdeep Singh Hundal, Yan Xiao, Jin Song Dong et al. |
|---|---|
| Primary category | eess.IV |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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