{"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/cross-modal-distillation-for-supervision","title":"Cross Modal Distillation for Supervision Transfer","arxiv_id":"1507.00448","date":"2015-07-02","proceeding":"CVPR 2016 6","authors":["Saurabh Gupta","Judy Hoffman","Jitendra Malik"],"abstract":"In this work we propose a technique that transfers supervision between images\nfrom different modalities. We use learned representations from a large labeled\nmodality as a supervisory signal for training representations for a new\nunlabeled paired modality. Our method enables learning of rich representations\nfor unlabeled modalities and can be used as a pre-training procedure for new\nmodalities with limited labeled data. We show experimental results where we\ntransfer supervision from labeled RGB images to unlabeled depth and optical\nflow images and demonstrate large improvements for both these cross modal\nsupervision transfers. Code, data and pre-trained models are available at\nhttps://github.com/s-gupta/fast-rcnn/tree/distillation","url_abs":"http://arxiv.org/abs/1507.00448v2","url_pdf":"http://arxiv.org/pdf/1507.00448v2.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":"cross-modal-distillation-for-supervision","repo_url":"https://github.com/s-gupta/fast-rcnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"caffe2","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1507.00448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1507.00448"}},"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. 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