{"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/what-object-should-i-use-task-driven-object","title":"What Object Should I Use? - Task Driven Object Detection","arxiv_id":"1904.03000","date":"2019-04-05","proceeding":"CVPR 2019 6","authors":["Johann Sawatzky","Yaser Souri","Christian Grund","Juergen Gall"],"abstract":"When humans have to solve everyday tasks, they simply pick the objects that\nare most suitable. While the question which object should one use for a\nspecific task sounds trivial for humans, it is very difficult to answer for\nrobots or other autonomous systems. This issue, however, is not addressed by\ncurrent benchmarks for object detection that focus on detecting object\ncategories. We therefore introduce the COCO-Tasks dataset which comprises about\n40,000 images where the most suitable objects for 14 tasks have been annotated.\nWe furthermore propose an approach that detects the most suitable objects for a\ngiven task. The approach builds on a Gated Graph Neural Network to exploit the\nappearance of each object as well as the global context of all present objects\nin the scene. In our experiments, we show that the proposed approach\noutperforms other approaches that are evaluated on the dataset like\nclassification or ranking approaches.","url_abs":"http://arxiv.org/abs/1904.03000v1","url_pdf":"http://arxiv.org/pdf/1904.03000v1.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":"what-object-should-i-use-task-driven-object","repo_url":"https://github.com/yassersouri/task-driven-object-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[{"slug":"coco-tasks","name":"COCO-Tasks","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.03000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03000"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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