{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dlbd-a-self-supervised-direct-learned-binary","title":"DLBD: A Self-Supervised Direct-Learned Binary Descriptor","arxiv_id":null,"date":"2023-01-01","proceeding":"CVPR 2023 1","authors":["Bin Xiao","Yang Hu","Bo Liu","Xiuli Bi","Weisheng Li","Xinbo Gao"],"abstract":"    For learning-based binary descriptors, the binarization process has not been well addressed. The reason is that the binarization blocks gradient back-propagation. Existing learning-based binary descriptors learn real-valued output, and then it is converted to binary descriptors by their proposed binarization processes. Since their binarization processes are not a component of the network, the learning-based binary descriptor cannot fully utilize the advances of deep learning. To solve this issue, we propose a model-agnostic plugin binary transformation layer (BTL), making the network directly generate binary descriptors. Then, we present the first self-supervised, direct-learned binary descriptor, dubbed DLBD. Furthermore, we propose ultra-wide temperature-scaled cross-entropy loss to adjust the distribution of learned descriptors in a larger range. Experiments demonstrate that the proposed BTL can substitute the previous binarization process. Our proposed DLBD outperforms SOTA on different tasks such as image retrieval and classification.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2023/html/Xiao_DLBD_A_Self-Supervised_Direct-Learned_Binary_Descriptor_CVPR_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2023/papers/Xiao_DLBD_A_Self-Supervised_Direct-Learned_Binary_Descriptor_CVPR_2023_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dlbd-a-self-supervised-direct-learned-binary","repo_url":"https://github.com/cqupt-cv/dlbd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"binarization","task_name":"Binarization"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}