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

• URL: http://arxiv.org/abs/2010.08644v1
• Date: Fri, 16 Oct 2020 22:06:43 GMT
• Title: Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels
• Authors: Xiangwei Shi, Seyran Khademi, Yunqiang Li, Jan van Gemert
• Abstract summary: Zoom-CAM captures fine-grained small-scale objects for various discriminative class instances. We focus on generating pixel-level pseudo-labels from class labels. For weakly supervised semantic segmentation our generated pseudo-labels improve a state of the art model by 1.1%.
• Score: 15.664293530106637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current weakly supervised object localization and segmentation rely on class-discriminative visualization techniques to generate pseudo-labels for pixel-level training. Such visualization methods, including class activation mapping (CAM) and Grad-CAM, use only the deepest, lowest resolution convolutional layer, missing all information in intermediate layers. We propose Zoom-CAM: going beyond the last lowest resolution layer by integrating the importance maps over all activations in intermediate layers. Zoom-CAM captures fine-grained small-scale objects for various discriminative class instances, which are commonly missed by the baseline visualization methods. We focus on generating pixel-level pseudo-labels from class labels. The quality of our pseudo-labels evaluated on the ImageNet localization task exhibits more than 2.8% improvement on top-1 error. For weakly supervised semantic segmentation our generated pseudo-labels improve a state of the art model by 1.1%.

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