{"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/simple-open-vocabulary-object-detection-with","title":"Simple Open-Vocabulary Object Detection with Vision Transformers","arxiv_id":"2205.06230","date":"2022-05-12","proceeding":null,"authors":["Matthias Minderer","Alexey Gritsenko","Austin Stone","Maxim Neumann","Dirk Weissenborn","Alexey Dosovitskiy","Aravindh Mahendran","Anurag Arnab","Mostafa Dehghani","Zhuoran Shen","Xiao Wang","Xiaohua Zhai","Thomas Kipf","Neil Houlsby"],"abstract":"Combining simple architectures with large-scale pre-training has led to massive improvements in image classification. For object detection, pre-training and scaling approaches are less well established, especially in the long-tailed and open-vocabulary setting, where training data is relatively scarce. In this paper, we propose a strong recipe for transferring image-text models to open-vocabulary object detection. We use a standard Vision Transformer architecture with minimal modifications, contrastive image-text pre-training, and end-to-end detection fine-tuning. Our analysis of the scaling properties of this setup shows that increasing image-level pre-training and model size yield consistent improvements on the downstream detection task. We provide the adaptation strategies and regularizations needed to attain very strong performance on zero-shot text-conditioned and one-shot image-conditioned object detection. Code and models are available on GitHub.","url_abs":"https://arxiv.org/abs/2205.06230v2","url_pdf":"https://arxiv.org/pdf/2205.06230v2.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":"simple-open-vocabulary-object-detection-with","repo_url":"https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"jax","reach":null},{"paper_slug":"simple-open-vocabulary-object-detection-with","repo_url":"https://github.com/yangyucheng000/University/tree/main/model-1/owlvit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"described-object-detection","task_name":"Described Object Detection"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"one-shot-object-detection","task_name":"One-Shot Object Detection"},{"task_slug":"open-vocabulary-object-detection","task_name":"Open Vocabulary Object Detection"},{"task_slug":"open-vocabulary-object-detection","task_name":"Open-vocabulary object detection"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/described-object-detection-on-description","task":"Described Object Detection","dataset":"Description Detection Dataset","model":"OWL-ViT-base","rank_in_archive_order":7,"of":8,"metrics":{"Intra-scenario ABS mAP":"8.8","Intra-scenario FULL mAP":"8.6","Intra-scenario PRES mAP":"8.5"},"uses_additional_data":false},{"leaderboard":"/sota/one-shot-object-detection-on-coco","task":"One-Shot Object Detection","dataset":"COCO (Common Objects in Context)","model":"OWL-ViT (R50+H/32)","rank_in_archive_order":1,"of":4,"metrics":{"AP 0.5":"41.8"},"uses_additional_data":false},{"leaderboard":"/sota/open-vocabulary-object-detection-on-lvis-v1-0","task":"Open Vocabulary Object Detection","dataset":"LVIS v1.0","model":"OWL-ViT (CLIP-L/14)","rank_in_archive_order":15,"of":28,"metrics":{"AP novel-LVIS base training":"25.6","AP novel-Unrestricted open-vocabulary training":"31.2"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2205.06230","atlas_url":"https://app.syntology.ai/?focus=2205.06230","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}