{"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/learning-to-measure-change-fully","title":"Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection","arxiv_id":"1810.09111","date":"2018-10-22","proceeding":null,"authors":["Enqiang Guo","Xinsha Fu","Jiawei Zhu","Min Deng","Yu Liu","Qing Zhu","Haifeng Li"],"abstract":"A critical challenge problem of scene change detection is that noisy changes\ngenerated by varying illumination, shadows and camera viewpoint make variances\nof a scene difficult to define and measure since the noisy changes and semantic\nones are entangled. Following the intuitive idea of detecting changes by\ndirectly comparing dissimilarities between a pair of features, we propose a\nnovel fully Convolutional siamese metric Network(CosimNet) to measure changes\nby customizing implicit metrics. To learn more discriminative metrics, we\nutilize contrastive loss to reduce the distance between the unchanged feature\npairs and to enlarge the distance between the changed feature pairs.\nSpecifically, to address the issue of large viewpoint differences, we propose\nThresholded Contrastive Loss (TCL) with a more tolerant strategy to punish\nnoisy changes. We demonstrate the effectiveness of the proposed approach with\nexperiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our\napproach is robust to lots of challenging conditions, such as illumination\nchanges, large viewpoint difference caused by camera motion and zooming. In\naddition, we incorporate the distance metric into the segmentation framework\nand validate the effectiveness through visualization of change maps and feature\ndistribution. The source code is available at\nhttps://github.com/gmayday1997/ChangeDet.","url_abs":"http://arxiv.org/abs/1810.09111v3","url_pdf":"http://arxiv.org/pdf/1810.09111v3.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":"learning-to-measure-change-fully","repo_url":"https://github.com/gmayday1997/ChangeDet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"learning-to-measure-change-fully","repo_url":"https://github.com/gmayday1997/SceneChangeDet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"scene-change-detection","task_name":"Scene Change Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.09111","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}