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
discriminative attributes for image classifiers more accurately than CAM and
other visual attribution baselines. CALM also shows performance improvements
over prior arts on the weakly-supervised object localization benchmarks. Our
code is available at this https URL

本文提出的 CALM 模型通过将经验校准步骤嵌入模型以及使用潜在变量来改进 CAM 算法的短处，在具有弱监督目标检测的基准测试中表现出更好的性能。