{"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/open-domain-visual-entity-recognition-towards","title":"Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities","arxiv_id":"2302.11154","date":"2023-02-22","proceeding":"ICCV 2023 1","authors":["Hexiang Hu","Yi Luan","Yang Chen","Urvashi Khandelwal","Mandar Joshi","Kenton Lee","Kristina Toutanova","Ming-Wei Chang"],"abstract":"Large-scale multi-modal pre-training models such as CLIP and PaLI exhibit strong generalization on various visual domains and tasks. However, existing image classification benchmarks often evaluate recognition on a specific domain (e.g., outdoor images) or a specific task (e.g., classifying plant species), which falls short of evaluating whether pre-trained foundational models are universal visual recognizers. To address this, we formally present the task of Open-domain Visual Entity recognitioN (OVEN), where a model need to link an image onto a Wikipedia entity with respect to a text query. We construct OVEN-Wiki by re-purposing 14 existing datasets with all labels grounded onto one single label space: Wikipedia entities. OVEN challenges models to select among six million possible Wikipedia entities, making it a general visual recognition benchmark with the largest number of labels. Our study on state-of-the-art pre-trained models reveals large headroom in generalizing to the massive-scale label space. We show that a PaLI-based auto-regressive visual recognition model performs surprisingly well, even on Wikipedia entities that have never been seen during fine-tuning. We also find existing pretrained models yield different strengths: while PaLI-based models obtain higher overall performance, CLIP-based models are better at recognizing tail entities.","url_abs":"https://arxiv.org/abs/2302.11154v2","url_pdf":"https://arxiv.org/pdf/2302.11154v2.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":"open-domain-visual-entity-recognition-towards","repo_url":"https://github.com/edchengg/oven_eval","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"open-domain-visual-entity-recognition-towards","repo_url":"https://github.com/open-vision-language/oven","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"fine-grained-image-recognition","task_name":"Fine-Grained Image Recognition"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"clip","method_name":"CLIP"}],"datasets_introduced":[{"slug":"oven","name":"OVEN","full_name":"Open-domain Visual Entity Recognition"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/fine-grained-image-recognition-on-oven","task":"Fine-Grained Image Recognition","dataset":"OVEN","model":"PaLI (17B)","rank_in_archive_order":2,"of":5,"metrics":{"Accuracy":"20.2"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-image-recognition-on-oven","task":"Fine-Grained Image Recognition","dataset":"OVEN","model":"PaLI (3B)","rank_in_archive_order":4,"of":5,"metrics":{"Accuracy":"11.8"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-image-recognition-on-oven","task":"Fine-Grained Image Recognition","dataset":"OVEN","model":"CLIP2CLIP","rank_in_archive_order":5,"of":5,"metrics":{"Accuracy":"5.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2302.11154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.11154"}},"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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