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.
Original language | English |
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Title of host publication | Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings |
Editors | Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 618-634 |
Number of pages | 17 |
ISBN (Print) | 9783030585549 |
DOIs | |
State | Published - 2020 |
Event | 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12360 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 16th European Conference on Computer Vision, ECCV 2020 |
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Country/Territory | United Kingdom |
City | Glasgow |
Period | 23/08/20 → 28/08/20 |
Bibliographical note
Publisher Copyright:© 2020, Springer Nature Switzerland AG.
Keywords
- Class Activation Mapping (CAM)
- Weakly Supervised Object Localization (WSOL)