TL;DR
This paper introduces CALM, an improved class activation mapping method that integrates attribution into the training process, leading to more accurate visual feature attribution and better weakly-supervised localization.
Contribution
CALM explicitly incorporates attribution into the training graph using latent variables and EM, enhancing interpretability and localization performance over CAM.
Findings
CALM outperforms CAM in identifying discriminative attributes.
CALM achieves better results on weakly-supervised object localization benchmarks.
The method improves attribution accuracy and interpretability.
Abstract
The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. Its simplicity and effectiveness have led to wide applications in the explanation of visual predictions and weakly-supervised localization tasks. However, CAM has its own shortcomings. The computation of attribution maps relies on ad-hoc calibration steps that are not part of the training computational graph, making it difficult for us to understand the real meaning of the attribution values. In this paper, we improve CAM by explicitly incorporating a latent variable encoding the location of the cue for recognition in the formulation, thereby subsuming the attribution map into the training computational graph. The resulting model, class activation latent mapping, or CALM, is trained with the expectation-maximization algorithm. Our experiments show that CALM identifies…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsClass-activation map
