{"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/rethinking-low-level-features-for-interest","title":"Rethinking Low-level Features for Interest Point Detection and Description","arxiv_id":null,"date":"2022-11-01","proceeding":"ACCV 2022 11","authors":["Changhao Wang","Guanwen Zhang","Zhengyun Cheng","and Wei Zhou"],"abstract":"Although great efforts have been made for interest point detection and description, the current learning-based methods that use high-level features from the higher layers of Convolutional Neural Networks (CNN) do not completely outperform the conventional methods. On the one hand, interest points are semantically ill-defined and high-level features that emphasize semantic information are not adequate to describe interest points; On the other hand, the existing methods using low-level information usually perform detection on multi-level feature maps, which is time consuming for real time applications. To address these problems, we propose a Low-level descriptor-Aware Network (LANet) for interest point detection and description in self-supervised learning. Specifically, the proposed LANet exploits the low-level features for interest point description while using high-level features for interest point detection. Experimental results demonstrate that LANet achieves state-of-the-art performance on the homography estimation benchmark. Notably, the proposed LANet is a front-end feature learning framework that can be deployed in downstream tasks that require interest points with high-quality descriptors.","url_abs":"https://openaccess.thecvf.com/content/ACCV2022/papers/Wang_Rethinking_Low-level_Features_for_Interest_Point_Detection_and_Description_ACCV_2022_paper.pdf","url_pdf":"https://openaccess.thecvf.com/content/ACCV2022/papers/Wang_Rethinking_Low-level_Features_for_Interest_Point_Detection_and_Description_ACCV_2022_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":"rethinking-low-level-features-for-interest","repo_url":"https://github.com/wangch-g/lanet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"homography-estimation","task_name":"Homography Estimation"},{"task_slug":"interest-point-detection","task_name":"Interest Point Detection"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}