{"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/diffusion-features-to-bridge-domain-gap-for","title":"Diffusion Features to Bridge Domain Gap for Semantic Segmentation","arxiv_id":"2406.00777","date":"2024-06-02","proceeding":null,"authors":["Yuxiang Ji","Boyong He","Chenyuan Qu","Zhuoyue Tan","Chuan Qin","Liaoni Wu"],"abstract":"Pre-trained diffusion models have demonstrated remarkable proficiency in synthesizing images across a wide range of scenarios with customizable prompts, indicating their effective capacity to capture universal features. Motivated by this, our study delves into the utilization of the implicit knowledge embedded within diffusion models to address challenges in cross-domain semantic segmentation. This paper investigates the approach that leverages the sampling and fusion techniques to harness the features of diffusion models efficiently. We propose DIffusion Feature Fusion (DIFF) as a backbone use for extracting and integrating effective semantic representations through the diffusion process. By leveraging the strength of text-to-image generation capability, we introduce a new training framework designed to implicitly learn posterior knowledge from it. Through rigorous evaluation in the contexts of domain generalization semantic segmentation, we establish that our methodology surpasses preceding approaches in mitigating discrepancies across distinct domains and attains the state-of-the-art (SOTA) benchmark.","url_abs":"https://arxiv.org/abs/2406.00777v2","url_pdf":"https://arxiv.org/pdf/2406.00777v2.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":"diffusion-features-to-bridge-domain-gap-for","repo_url":"https://github.com/Yux1angJi/DIFF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"text-to-image-generation-1","task_name":"Text to Image Generation"},{"task_slug":"text-to-image-generation","task_name":"Text-to-Image Generation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-generalization-on-gta-to-avg","task":"Domain Generalization","dataset":"GTA-to-Avg(Cityscapes,BDD,Mapillary)","model":"DIFF","rank_in_archive_order":10,"of":24,"metrics":{"mIoU":"57.15"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-gta5-to-cityscapes","task":"Domain Generalization","dataset":"GTA5-to-Cityscapes","model":"DIFF","rank_in_archive_order":5,"of":8,"metrics":{"mIoU":"58.01"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.00777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00777"}},"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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