{"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/ov-dino-unified-open-vocabulary-detection","title":"OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion","arxiv_id":"2407.07844","date":"2024-07-10","proceeding":null,"authors":["Hao Wang","Pengzhen Ren","Zequn Jie","Xiao Dong","Chengjian Feng","Yinlong Qian","Lin Ma","Dongmei Jiang","YaoWei Wang","Xiangyuan Lan","Xiaodan Liang"],"abstract":"Open-vocabulary detection is a challenging task due to the requirement of detecting objects based on class names, including those not encountered during training. Existing methods have shown strong zero-shot detection capabilities through pre-training and pseudo-labeling on diverse large-scale datasets. However, these approaches encounter two main challenges: (i) how to effectively eliminate data noise from pseudo-labeling, and (ii) how to efficiently leverage the language-aware capability for region-level cross-modality fusion and alignment. To address these challenges, we propose a novel unified open-vocabulary detection method called OV-DINO, which is pre-trained on diverse large-scale datasets with language-aware selective fusion in a unified framework. Specifically, we introduce a Unified Data Integration (UniDI) pipeline to enable end-to-end training and eliminate noise from pseudo-label generation by unifying different data sources into detection-centric data format. In addition, we propose a Language-Aware Selective Fusion (LASF) module to enhance the cross-modality alignment through a language-aware query selection and fusion process. We evaluate the performance of the proposed OV-DINO on popular open-vocabulary detection benchmarks, achieving state-of-the-art results with an AP of 50.6% on the COCO benchmark and 40.1% on the LVIS benchmark in a zero-shot manner, demonstrating its strong generalization ability. Furthermore, the fine-tuned OV-DINO on COCO achieves 58.4% AP, outperforming many existing methods with the same backbone. The code for OV-DINO is available at https://github.com/wanghao9610/OV-DINO.","url_abs":"https://arxiv.org/abs/2407.07844v2","url_pdf":"https://arxiv.org/pdf/2407.07844v2.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":"ov-dino-unified-open-vocabulary-detection","repo_url":"https://github.com/wanghao9610/ov-dino","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"zero-shot-object-detection","task_name":"Zero-Shot Object Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/zero-shot-object-detection-on-lvis-v1-0","task":"Zero-Shot Object Detection","dataset":"LVIS v1.0 minival","model":"OV-DINO-T (without LVIS data, swin tiny)","rank_in_archive_order":6,"of":11,"metrics":{"AP":"40.1"},"uses_additional_data":true},{"leaderboard":"/sota/zero-shot-object-detection-on-lvis-v1-0-val","task":"Zero-Shot Object Detection","dataset":"LVIS v1.0 val","model":"OV-DINO-T (without LVIS data, swin tiny)","rank_in_archive_order":6,"of":9,"metrics":{"AP":"32.9"},"uses_additional_data":true},{"leaderboard":"/sota/zero-shot-object-detection-on-mscoco","task":"Zero-Shot Object Detection","dataset":"MSCOCO","model":"OV-DINO-T (without COCO data)","rank_in_archive_order":5,"of":7,"metrics":{"AP":"50.6"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2407.07844","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}