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Evidence record 4717 · automatically gathered

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

Record details

Published: 8 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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How to cite this record

ethics.ai (8 May 2026), “CAMAL: Improving Attention Alignment and Faithfulness with Segmentation Masks,” evidence record 4717, https://ethics.ai/record/4717 (originally published by arXiv).

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