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Current weakly supervised object localization and segmentation rely onclass-discriminative visualization techniques to generate pseudo-labels forpixel-level training. Such visualization methods, including class activationmapping (CAM) and Grad-CAM, use only the deepest, lowest resolutionconvolutional layer, missing all information in intermediate layers. We proposeZoom-CAM: going beyond the last lowest resolution layer by integrating theimportance maps over all activations in intermediate layers. Zoom-CAM capturesfine-grained small-scale objects for various discriminative class instances,which are commonly missed by the baseline visualization methods. We focus ongenerating pixel-level pseudo-labels from class labels. The quality of ourpseudo-labels evaluated on the ImageNet localization task exhibits more than2.8% improvement on top-1 error. For weakly supervised semantic segmentationour generated pseudo-labels improve a state of the art model by 1.1%.

Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels