{"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/on-the-utility-of-context-or-the-lack-thereof","title":"Exploring the Bounds of the Utility of Context for Object Detection","arxiv_id":"1711.05471","date":"2017-11-15","proceeding":"CVPR 2019 6","authors":["Ehud Barnea","Ohad Ben-Shahar"],"abstract":"The recurring context in which objects appear holds valuable information that\ncan be employed to predict their existence. This intuitive observation indeed\nled many researchers to endow appearance-based detectors with explicit\nreasoning about context. The underlying thesis suggests that stronger\ncontextual relations would facilitate greater improvements in detection\ncapacity. In practice, however, the observed improvement in many cases is\nmodest at best, and often only marginal. In this work we seek to improve our\nunderstanding of this phenomenon, in part by pursuing an opposite approach.\nInstead of attempting to improve detection scores by employing context, we\ntreat the utility of context as an optimization problem: to what extent can\ndetection scores be improved by considering context or any other kind of\nadditional information? With this approach we explore the bounds on improvement\nby using contextual relations between objects and provide a tool for\nidentifying the most helpful ones. We show that simple co-occurrence relations\ncan often provide large gains, while in other cases a significant improvement\nis simply impossible or impractical with either co-occurrence or more precise\nspatial relations. To better understand these results we then analyze the\nability of context to handle different types of false detections, revealing\nthat tested contextual information cannot ameliorate localization errors,\nseverely limiting its gains. These and additional insights further our\nunderstanding on where and why utilization of context for object detection\nsucceeds and fails.","url_abs":"http://arxiv.org/abs/1711.05471v4","url_pdf":"http://arxiv.org/pdf/1711.05471v4.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":"on-the-utility-of-context-or-the-lack-thereof","repo_url":"https://github.com/EhudBarnea/ContextAnalysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.05471","atlas_url":"https://app.syntology.ai/?focus=1711.05471","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}