Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects. To overcome these challenges, we propose a novel framework, namely Explicit Pseudo-pixel Supervision (EPS), which learns from pixel-level feedback by combining two weak supervisions; the image-level label provides the object identity via the localization map and the saliency map from the off-the-shelf saliency detection model offers rich boundaries. We devise a joint training strategy to fully utilize the complementary relationship between both information. Our method can obtain accurate object boundaries and discard co-occurring pixels, thereby significantly improving the quality of pseudo-masks. Experimental results show that the proposed method remarkably outperforms existing methods by resolving key challenges of WSSS and achieves the new state-of-the-art performance on both PASCAL VOC 2012 and MS COCO 2014 datasets. The code is available at https://github.com/halbielee/EPS.
|Title of host publication||Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021|
|Publisher||IEEE Computer Society|
|Number of pages||11|
|Publication status||Published - 2021|
|Event||2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 - Virtual, Online, United States|
Duration: 2021 Jun 19 → 2021 Jun 25
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021|
|Period||21/6/19 → 21/6/25|
Bibliographical noteFunding Information:
Acknowledgements. We thank Duhyeon Bang and Jun-suk Choe for the feedback. This research was supported by the Basic Science Research Program through the NRF Korea funded by the MSIP (NRF-2019R1A2C2006123, 2020R1A4A1016619), the IITP grant funded by the MSIT (2020-0-01361, Artificial Intelligence Graduate School Program (YONSEI UNIVERSITY)), and the Korea Medical Device Development Fund grant funded by the Korean government (Project Number: 202011D06).
© 2021 IEEE
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition