{"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/unsupervised-representation-learning-by","title":"Unsupervised Representation Learning by Sorting Sequences","arxiv_id":"1708.01246","date":"2017-08-03","proceeding":"ICCV 2017 10","authors":["Hsin-Ying Lee","Jia-Bin Huang","Maneesh Singh","Ming-Hsuan Yang"],"abstract":"We present an unsupervised representation learning approach using videos\nwithout semantic labels. We leverage the temporal coherence as a supervisory\nsignal by formulating representation learning as a sequence sorting task. We\ntake temporally shuffled frames (i.e., in non-chronological order) as inputs\nand train a convolutional neural network to sort the shuffled sequences.\nSimilar to comparison-based sorting algorithms, we propose to extract features\nfrom all frame pairs and aggregate them to predict the correct order. As\nsorting shuffled image sequence requires an understanding of the statistical\ntemporal structure of images, training with such a proxy task allows us to\nlearn rich and generalizable visual representation. We validate the\neffectiveness of the learned representation using our method as pre-training on\nhigh-level recognition problems. The experimental results show that our method\ncompares favorably against state-of-the-art methods on action recognition,\nimage classification and object detection tasks.","url_abs":"http://arxiv.org/abs/1708.01246v1","url_pdf":"http://arxiv.org/pdf/1708.01246v1.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":"unsupervised-representation-learning-by","repo_url":"https://github.com/HsinYingLee/OPN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"caffe2","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"self-supervised-action-recognition","task_name":"Self-Supervised Action Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/self-supervised-action-recognition-on-hmdb51","task":"Self-Supervised Action Recognition","dataset":"HMDB51","model":"OPN (VGG-M-2048)","rank_in_archive_order":46,"of":48,"metrics":{"Frozen":"false","Pre-Training Dataset":"UCF101","Top-1 Accuracy":"23.8"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1708.01246","atlas_url":"https://app.syntology.ai/?focus=1708.01246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.01246"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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