{"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/text-image-alignment-for-diffusion-based","title":"Text-image Alignment for Diffusion-based Perception","arxiv_id":"2310.00031","date":"2023-09-29","proceeding":"CVPR 2024 1","authors":["Neehar Kondapaneni","Markus Marks","Manuel Knott","Rogerio Guimaraes","Pietro Perona"],"abstract":"Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: https://www.vision.caltech.edu/tadp/. Code: https://github.com/damaggu/TADP.","url_abs":"https://arxiv.org/abs/2310.00031v3","url_pdf":"https://arxiv.org/pdf/2310.00031v3.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":"text-image-alignment-for-diffusion-based","repo_url":"https://github.com/damaggu/tadp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"text-image-alignment-for-diffusion-based","repo_url":"https://github.com/nkondapa/RSVC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"weakly-supervised-object-detection","task_name":"Weakly Supervised Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"fpn","method_name":"FPN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/monocular-depth-estimation-on-nyu-depth-v2","task":"Monocular Depth Estimation","dataset":"NYU-Depth V2","model":"TADP","rank_in_archive_order":17,"of":85,"metrics":{"Delta < 1.25":"0.976","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.999","RMSE":"0.225","absolute relative error":"0.062","log 10":"0.027"},"uses_additional_data":true},{"leaderboard":"/sota/semantic-segmentation-on-ade20k","task":"Semantic Segmentation","dataset":"ADE20K","model":"TADP","rank_in_archive_order":44,"of":235,"metrics":{"Validation mIoU":"55.9"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-nighttime-driving","task":"Semantic Segmentation","dataset":"Nighttime Driving","model":"TADP","rank_in_archive_order":1,"of":13,"metrics":{"mIoU":"60.8"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-pascal-voc-2012-val","task":"Semantic Segmentation","dataset":"PASCAL VOC 2012 val","model":"TADP","rank_in_archive_order":2,"of":29,"metrics":{"mIoU":"87.11%"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-comic2k","task":"Weakly Supervised Object Detection","dataset":"Comic2k","model":"TADP","rank_in_archive_order":2,"of":8,"metrics":{"MAP":"57.4"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-1","task":"Weakly Supervised Object Detection","dataset":"Watercolor2k","model":"TADP","rank_in_archive_order":1,"of":12,"metrics":{"MAP":"72.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2310.00031","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}