{"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/dino-mc-self-supervised-contrastive-learning","title":"Extending global-local view alignment for self-supervised learning with remote sensing imagery","arxiv_id":"2303.06670","date":"2023-03-12","proceeding":null,"authors":["Xinye Wanyan","Sachith Seneviratne","Shuchang Shen","Michael Kirley"],"abstract":"Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature representations by formulating a pretext task that generates pseudo-labels for massive unlabeled data to provide supervision for training. While prior studies have explored multiple self-supervised learning techniques in remote sensing domain, pretext tasks based on local-global view alignment remain underexplored, despite achieving state-of-the-art results on natural imagery. Inspired by DINO, which employs an effective representation learning structure with knowledge distillation based on global-local view alignment, we formulate two pretext tasks for self-supervised learning on remote sensing imagery (SSLRS). Using these tasks, we explore the effectiveness of positive temporal contrast as well as multi-sized views on SSLRS. We extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size in order to alleviate the limited variation in object size observed in remote sensing imagery. Our experiments demonstrate that even when pre-trained on only 10% of the dataset, DINO-MC performs on par or better than existing state-of-the-art SSLRS methods on multiple remote sensing tasks, while using less computational resources. All codes, models, and results are released at https://github.com/WennyXY/DINO-MC.","url_abs":"https://arxiv.org/abs/2303.06670v2","url_pdf":"https://arxiv.org/pdf/2303.06670v2.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":"dino-mc-self-supervised-contrastive-learning","repo_url":"https://github.com/wennyxy/dino-mc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"linear-probe-classification","task_name":"Linear-Probe Classification"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"multi-label-image-classification","task_name":"Multi-Label Image Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/change-detection-on-oscd-13ch","task":"Change Detection","dataset":"OSCD - 13ch","model":"DINO-MC (WRN-50)","rank_in_archive_order":3,"of":6,"metrics":{"F1":"52.7","Precision":"49.99"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-eurosat","task":"Image Classification","dataset":"EuroSAT","model":"DINO-MC (Wide ResNet)","rank_in_archive_order":8,"of":15,"metrics":{"Accuracy (%)":"98.78"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-eurosat","task":"Image Classification","dataset":"EuroSAT","model":"DINO-MC (WRN linear eval))","rank_in_archive_order":14,"of":15,"metrics":{"Accuracy (%)":"95.7"},"uses_additional_data":true},{"leaderboard":"/sota/multi-label-image-classification-on","task":"Multi-Label Image Classification","dataset":"BigEarthNet","model":"DINO-MC","rank_in_archive_order":5,"of":10,"metrics":{"mAP (micro)":"88.75","official split":"No"},"uses_additional_data":true},{"leaderboard":"/sota/multi-label-image-classification-on-1","task":"Multi-Label Image Classification","dataset":"BigEarthNet-10%","model":"DINO-MC","rank_in_archive_order":1,"of":1,"metrics":{"mean average precision":"84.20"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.06670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06670"}},"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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