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Rethinking Class Activation Mapping for Weakly Supervised Object Localization

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12360)

Abstract

Weakly supervised object localization (WSOL) is a task of localizing an object in an image only using image-level labels. To tackle the WSOL problem, most previous studies have followed the conventional class activation mapping (CAM) pipeline: (i) training CNNs for a classification objective, (ii) generating a class activation map via global average pooling (GAP) on feature maps, and (iii) extracting bounding boxes by thresholding based on the maximum value of the class activation map. In this work, we reveal the current CAM approach suffers from three fundamental issues: (i) the bias of GAP that assigns a higher weight to a channel with a small activation area, (ii) negatively weighted activations inside the object regions and (iii) instability from the use of the maximum value of a class activation map as a thresholding reference. They collectively cause the problem that the localization to be highly limited to small regions of an object. We propose three simple but robust techniques that alleviate the problems, including thresholded average pooling, negative weight clamping, and percentile as a standard for thresholding. Our solutions are universally applicable to any WSOL methods using CAM and improve their performance drastically. As a result, we achieve the new state-of-the-art performance on three benchmark datasets of CUB-200–2011, ImageNet-1K, and OpenImages30K.

Keywords

Weakly Supervised Object Localization (WSOL) Class Activation Mapping (CAM) 

Notes

Acknowledgements

We appreciate Hyunwoo Kim and Jinhwan Seo for their valuable comments. This work was supported by AIR Lab (AI Research Lab) in Hyundai Motor Company through HMC-SNU AI Consortium Fund, and the ICT R&D program of MSIT/IITP (No. 2019-0-01309, Development of AI technology for guidance of a mobile robot to its goal with uncertain maps in indoor/outdoor environments and No.2019-0-01082, SW StarLab).

Supplementary material

504470_1_En_37_MOESM1_ESM.pdf (10.6 mb)
Supplementary material 1 (pdf 10897 KB)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSeoul National UniversitySeoulKorea

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