{"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/autofocusformer-image-segmentation-off-the","title":"AutoFocusFormer: Image Segmentation off the Grid","arxiv_id":"2304.12406","date":"2023-04-24","proceeding":"CVPR 2023 1","authors":["Chen Ziwen","Kaushik Patnaik","Shuangfei Zhai","Alvin Wan","Zhile Ren","Alex Schwing","Alex Colburn","Li Fuxin"],"abstract":"Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small objects. Yet, the commonly used successive grid downsampling strategy in convolutional deep networks treats all areas equally. Hence, small objects are represented in very few spatial locations, leading to worse results in tasks such as segmentation. Intuitively, retaining more pixels representing small objects during downsampling helps to preserve important information. To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task. Since adaptive downsampling generates a set of pixels irregularly distributed on the image plane, we abandon the classic grid structure. Instead, we develop a novel point-based local attention block, facilitated by a balanced clustering module and a learnable neighborhood merging module, which yields representations for our point-based versions of state-of-the-art segmentation heads. Experiments show that our AutoFocusFormer (AFF) improves significantly over baseline models of similar sizes.","url_abs":"https://arxiv.org/abs/2304.12406v2","url_pdf":"https://arxiv.org/pdf/2304.12406v2.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":"autofocusformer-image-segmentation-off-the","repo_url":"https://github.com/apple/ml-autofocusformer","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/instance-segmentation-on-cityscapes-val","task":"Instance Segmentation","dataset":"Cityscapes val","model":"AFF-Base (single-scale, point-based Mask2Former)","rank_in_archive_order":4,"of":17,"metrics":{"AP50":"74.2","mask AP":"46.2"},"uses_additional_data":false},{"leaderboard":"/sota/instance-segmentation-on-cityscapes-val","task":"Instance Segmentation","dataset":"Cityscapes val","model":"AFF-Small (single-scale, point-based Mask2Former)","rank_in_archive_order":8,"of":17,"metrics":{"AP50":"72.8","mask AP":"44.0"},"uses_additional_data":false},{"leaderboard":"/sota/panoptic-segmentation-on-cityscapes-val","task":"Panoptic Segmentation","dataset":"Cityscapes val","model":"AFF-Base (single-scale, point-based Mask2Former)","rank_in_archive_order":9,"of":37,"metrics":{"AP":"46.2","PQ":"67.7","PQst":"71.5","PQth":"62.5","mIoU":"83.0"},"uses_additional_data":false},{"leaderboard":"/sota/panoptic-segmentation-on-cityscapes-val","task":"Panoptic Segmentation","dataset":"Cityscapes val","model":"AFF-Small (single-scale, point-based Mask2Former)","rank_in_archive_order":14,"of":37,"metrics":{"AP":"44.2","PQ":"66.9","PQst":"70.8","PQth":"61.5","mIoU":"82.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2304.12406","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}