{"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/end-to-end-instance-segmentation-with","title":"End-to-End Instance Segmentation with Recurrent Attention","arxiv_id":"1605.09410","date":"2016-05-30","proceeding":"CVPR 2017 7","authors":["Mengye Ren","Richard S. Zemel"],"abstract":"While convolutional neural networks have gained impressive success recently\nin solving structured prediction problems such as semantic segmentation, it\nremains a challenge to differentiate individual object instances in the scene.\nInstance segmentation is very important in a variety of applications, such as\nautonomous driving, image captioning, and visual question answering. Techniques\nthat combine large graphical models with low-level vision have been proposed to\naddress this problem; however, we propose an end-to-end recurrent neural\nnetwork (RNN) architecture with an attention mechanism to model a human-like\ncounting process, and produce detailed instance segmentations. The network is\njointly trained to sequentially produce regions of interest as well as a\ndominant object segmentation within each region. The proposed model achieves\ncompetitive results on the CVPPP, KITTI, and Cityscapes datasets.","url_abs":"http://arxiv.org/abs/1605.09410v5","url_pdf":"http://arxiv.org/pdf/1605.09410v5.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":"end-to-end-instance-segmentation-with","repo_url":"https://github.com/renmengye/rec-attend-public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.09410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.09410"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/renmengye/rec-attend-public","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"98f85d81471fea59","entry":"get_save_var","repo":"renmengye/rec-attend-public","repo_kind":"listed","path":"fg_model.py","file_url":"https://github.com/renmengye/rec-attend-public/blob/HEAD/fg_model.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"98f85d81471fea59"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}