Ding, Z., Wang, A., Chen, H. et al. (5 more authors) (2023) Exploring structured semantic prior for multi label recognition with incomplete labels. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Proceedings. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 17-24 Jun 2023, Vancouver, BC, Canada. Institute of Electrical and Electronics Engineers , pp. 3398-3407. ISBN 9798350301304
Abstract
Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, i.e., CLIP [22], to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label corre-spondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCP-Net), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly out-performs existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://github.com/jameslahm/SCPNet.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a paper published in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Proceedings is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Recognition: Categorization; detection; retrieval |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Apr 2023 16:10 |
Last Modified: | 15 Sep 2023 15:21 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/CVPR52729.2023.00331 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198540 |
Download
Filename: 9122_exploring_structured_semantic_-Camera-ready PDF.pdf
Licence: CC-BY 4.0