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Backpropagation image saliency aims at explaining model predictions byestimating model-centric importance of individual pixels in the input. However,class-insensitivity of the earlier layers in a network only allows saliencycomputation with low resolution activation maps of the deeper layers, resultingin compromised image saliency. Remedifying this can lead to sanity failures. Wepropose CAMERAS, a technique to compute high-fidelity backpropagation saliencymaps without requiring any external priors and preserving the map sanity. Ourmethod systematically performs multi-scale accumulation and fusion of theactivation maps and backpropagated gradients to compute precise saliency maps.From accurate image saliency to articulation of relative importance of inputfeatures for different models, and precise discrimination between modelperception of visually similar objects, our high-resolution mapping offersmultiple novel insights into the black-box deep visual models, which arepresented in the paper. We also demonstrate the utility of our saliency maps inadversarial setup by drastically reducing the norm of attack signals byfocusing them on the precise regions identified by our maps. Our method alsoinspires new evaluation metrics and a sanity check for this developing researchdirection. Code is available here https://github.com/VisMIL/CAMERAS

CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency