{"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/exploring-phrase-level-grounding-with-text-to","title":"Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model","arxiv_id":"2407.05352","date":"2024-07-07","proceeding":null,"authors":["Danni Yang","Ruohan Dong","Jiayi Ji","Yiwei Ma","Haowei Wang","Xiaoshuai Sun","Rongrong Ji"],"abstract":"Recently, diffusion models have increasingly demonstrated their capabilities in vision understanding. By leveraging prompt-based learning to construct sentences, these models have shown proficiency in classification and visual grounding tasks. However, existing approaches primarily showcase their ability to perform sentence-level localization, leaving the potential for leveraging contextual information for phrase-level understanding largely unexplored. In this paper, we utilize Panoptic Narrative Grounding (PNG) as a proxy task to investigate this capability further. PNG aims to segment object instances mentioned by multiple noun phrases within a given narrative text. Specifically, we introduce the DiffPNG framework, a straightforward yet effective approach that fully capitalizes on the diffusion's architecture for segmentation by decomposing the process into a sequence of localization, segmentation, and refinement steps. The framework initially identifies anchor points using cross-attention mechanisms and subsequently performs segmentation with self-attention to achieve zero-shot PNG. Moreover, we introduce a refinement module based on SAM to enhance the quality of the segmentation masks. Our extensive experiments on the PNG dataset demonstrate that DiffPNG achieves strong performance in the zero-shot PNG task setting, conclusively proving the diffusion model's capability for context-aware, phrase-level understanding. Source code is available at \\url{https://github.com/nini0919/DiffPNG}.","url_abs":"https://arxiv.org/abs/2407.05352v1","url_pdf":"https://arxiv.org/pdf/2407.05352v1.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":"exploring-phrase-level-grounding-with-text-to","repo_url":"https://github.com/nini0919/diffpng","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"visual-grounding","task_name":"Visual Grounding"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"sam","method_name":"SAM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2407.05352","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}