{"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/orbit-a-real-world-few-shot-dataset-for","title":"ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition","arxiv_id":"2104.03841","date":"2021-04-08","proceeding":"ICCV 2021 10","authors":["Daniela Massiceti","Luisa Zintgraf","John Bronskill","Lida Theodorou","Matthew Tobias Harris","Edward Cutrell","Cecily Morrison","Katja Hofmann","Simone Stumpf"],"abstract":"Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset and benchmark, grounded in the real-world application of teachable object recognizers for people who are blind/low-vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind/low-vision on their mobile phones. The benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions. We set the benchmark's first state-of-the-art and show there is massive scope for further innovation, holding the potential to impact a broad range of real-world vision applications including tools for the blind/low-vision community. We release the dataset at https://doi.org/10.25383/city.14294597 and benchmark code at https://github.com/microsoft/ORBIT-Dataset.","url_abs":"https://arxiv.org/abs/2104.03841v5","url_pdf":"https://arxiv.org/pdf/2104.03841v5.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":"orbit-a-real-world-few-shot-dataset-for","repo_url":"https://github.com/microsoft/ORBIT-Dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[{"slug":"orbit","name":"ORBIT","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-orbit-clean","task":"Few-Shot Image Classification","dataset":"ORBIT Clean Video Evaluation","model":"MAML","rank_in_archive_order":2,"of":2,"metrics":{"Frame accuracy":"70.58"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-orbit","task":"Few-Shot Image Classification","dataset":"ORBIT Clutter Video Evaluation","model":"FineTuner","rank_in_archive_order":3,"of":3,"metrics":{"Frame accuracy":"53.73"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2104.03841","atlas_url":"https://app.syntology.ai/?focus=2104.03841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.03841"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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