{"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-vocabulary-panoptic-segmentation-using","title":"Open-Vocabulary Panoptic Segmentation Using BERT Pre-Training of Vision-Language Multiway Transformer Model","arxiv_id":"2412.18917","date":"2024-12-25","proceeding":null,"authors":["Yi-Chia Chen","Wei-Hua Li","Chu-Song Chen"],"abstract":"Open-vocabulary panoptic segmentation remains a challenging problem. One of the biggest difficulties lies in training models to generalize to an unlimited number of classes using limited categorized training data. Recent popular methods involve large-scale vision-language pre-trained foundation models, such as CLIP. In this paper, we propose OMTSeg for open-vocabulary segmentation using another large-scale vision-language pre-trained model called BEiT-3 and leveraging the cross-modal attention between visual and linguistic features in BEiT-3 to achieve better performance. Experiments result demonstrates that OMTSeg performs favorably against state-of-the-art models.","url_abs":"https://arxiv.org/abs/2412.18917v1","url_pdf":"https://arxiv.org/pdf/2412.18917v1.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-vocabulary-panoptic-segmentation-using","repo_url":"https://github.com/ai-application-and-integration-lab/omtseg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"open-vocabulary-panoptic-segmentation","task_name":"Open Vocabulary Panoptic Segmentation"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"clip","method_name":"CLIP"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}