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2020 | OriginalPaper | Chapter

Box2Seg: Attention Weighted Loss and Discriminative Feature Learning for Weakly Supervised Segmentation

Authors: Viveka Kulharia, Siddhartha Chandra, Amit Agrawal, Philip Torr, Ambrish Tyagi

Published in: Computer Vision – ECCV 2020

Publisher: Springer International Publishing

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Abstract

We propose a weakly supervised approach to semantic segmentation using bounding box annotations. Bounding boxes are treated as noisy labels for the foreground objects. We predict a per-class attention map that saliently guides the per-pixel cross entropy loss to focus on foreground pixels and refines the segmentation boundaries. This avoids propagating erroneous gradients due to incorrect foreground labels on the background. Additionally, we learn pixel embeddings to simultaneously optimize for high intra-class feature affinity while increasing discrimination between features across different classes. Our method, Box2Seg, achieves state-of-the-art segmentation accuracy on PASCAL VOC 2012 by significantly improving the mIOU metric by \(2.1\%\) compared to previous weakly supervised approaches. Our weakly supervised approach is comparable to the recent fully supervised methods when fine-tuned with limited amount of pixel-level annotations. Qualitative results and ablation studies show the benefit of different loss terms on the overall performance.

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Appendix
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Metadata
Title
Box2Seg: Attention Weighted Loss and Discriminative Feature Learning for Weakly Supervised Segmentation
Authors
Viveka Kulharia
Siddhartha Chandra
Amit Agrawal
Philip Torr
Ambrish Tyagi
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-58583-9_18

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