{"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/look-into-person-self-supervised-structure","title":"Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing","arxiv_id":"1703.05446","date":"2017-03-16","proceeding":"CVPR 2017 7","authors":["Ke Gong","Xiaodan Liang","Dongyu Zhang","Xiaohui Shen","Liang Lin"],"abstract":"Human parsing has recently attracted a lot of research interests due to its\nhuge application potentials. However existing datasets have limited number of\nimages and annotations, and lack the variety of human appearances and the\ncoverage of challenging cases in unconstrained environment. In this paper, we\nintroduce a new benchmark \"Look into Person (LIP)\" that makes a significant\nadvance in terms of scalability, diversity and difficulty, a contribution that\nwe feel is crucial for future developments in human-centric analysis. This\ncomprehensive dataset contains over 50,000 elaborately annotated images with 19\nsemantic part labels, which are captured from a wider range of viewpoints,\nocclusions and background complexity. Given these rich annotations we perform\ndetailed analyses of the leading human parsing approaches, gaining insights\ninto the success and failures of these methods. Furthermore, in contrast to the\nexisting efforts on improving the feature discriminative capability, we solve\nhuman parsing by exploring a novel self-supervised structure-sensitive learning\napproach, which imposes human pose structures into parsing results without\nresorting to extra supervision (i.e., no need for specifically labeling human\njoints in model training). Our self-supervised learning framework can be\ninjected into any advanced neural networks to help incorporate rich high-level\nknowledge regarding human joints from a global perspective and improve the\nparsing results. Extensive evaluations on our LIP and the public\nPASCAL-Person-Part dataset demonstrate the superiority of our method.","url_abs":"http://arxiv.org/abs/1703.05446v2","url_pdf":"http://arxiv.org/pdf/1703.05446v2.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":"look-into-person-self-supervised-structure","repo_url":"https://github.com/Engineering-Course/LIP_SSL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"human-parsing","task_name":"Human Parsing"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[{"slug":"lip","name":"LIP","full_name":"Look into Person"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-lip-val","task":"Semantic Segmentation","dataset":"LIP val","model":"Attention+SSL (ResNet-101)","rank_in_archive_order":13,"of":13,"metrics":{"mIoU":"44.73%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.05446","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}