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For object detection, classical approaches for data\naugmentation consist of generating images obtained by basic geometrical\ntransformations and color changes of original training images. In this work, we\ngo one step further and leverage segmentation annotations to increase the\nnumber of object instances present on training data. For this approach to be\nsuccessful, we show that modeling appropriately the visual context surrounding\nobjects is crucial to place them in the right environment. Otherwise, we show\nthat the previous strategy actually hurts. With our context model, we achieve\nsignificant mean average precision improvements when few labeled examples are\navailable on the VOC'12 benchmark.","url_abs":"http://arxiv.org/abs/1807.07428v1","url_pdf":"http://arxiv.org/pdf/1807.07428v1.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":"modeling-visual-context-is-key-to-augmenting","repo_url":"https://github.com/dvornikita/context_aug","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"modeling-visual-context-is-key-to-augmenting","repo_url":"https://github.com/linxi159/context_aug","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.07428","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.07428"}},"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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