  As deep vision models' popularity rapidly increases, there is a growing
emphasis on explanations for model predictions. The inherently explainable
attribution method aims to enhance the understanding of model behavior by
identifying the important regions in images that significantly contribute to
predictions. It is achieved by cooperatively training a selector (generating an
attribution map to identify important features) and a predictor (making
predictions using the identified features). Despite many advancements, existing
methods suffer from the incompleteness problem, where discriminative features
are masked out, and the interlocking problem, where the non-optimized selector
initially selects noise, causing the predictor to fit on this noise and
perpetuate the cycle. To address these problems, we introduce a new objective
that discourages the presence of discriminative features in the masked-out
regions thus enhancing the comprehensiveness of feature selection. A
pre-trained detector is introduced to detect discriminative features in the
masked-out region. If the selector selects noise instead of discriminative
features, the detector can observe and break the interlocking situation by
penalizing the selector. Extensive experiments show that our model makes
accurate predictions with higher accuracy than the regular black-box model, and
produces attribution maps with high feature coverage, localization ability,
fidelity and robustness. Our code will be available at
\href{https://github.com/Zood123/COMET}{https://github.com/Zood123/COMET}.
