MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization

• URL: http://arxiv.org/abs/2104.14375v1
• Date: Thu, 29 Apr 2021 14:39:53 GMT
• Title: MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization
• Authors: Kaili Wang, Jose Oramas, Tinne Tuytelaars
• Abstract summary: We propose two representation regularization mechanisms for weakly supervised object localization. Full Region Regularization tries to maximize the coverage of the localization map inside the object region, and Common Region Regularization minimizes the activations occurring in background regions. We evaluate the two regularizations on the ImageNet, CUB-200-2011 and OpenImages-segmentation datasets, and show that the proposed regularizations tackle both problems, outperforming the state-of-the-art by a significant margin.
• Score: 46.36600006968488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the most common problems of weakly supervised object localization is that of inaccurate object coverage. In the context of state-of-the-art methods based on Class Activation Mapping, this is caused either by localization maps which focus, exclusively, on the most discriminative region of the objects of interest or by activations occurring in background regions. To address these two problems, we propose two representation regularization mechanisms: Full Region Regularizationwhich tries to maximize the coverage of the localization map inside the object region, and Common Region Regularization which minimizes the activations occurring in background regions. We evaluate the two regularizations on the ImageNet, CUB-200-2011 and OpenImages-segmentation datasets, and show that the proposed regularizations tackle both problems, outperforming the state-of-the-art by a significant margin.

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