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Inspired by a\ntop-down human visual attention model, we propose a new backpropagation scheme,\ncalled Excitation Backprop, to pass along top-down signals downwards in the\nnetwork hierarchy via a probabilistic Winner-Take-All process. Furthermore, we\nintroduce the concept of contrastive attention to make the top-down attention\nmaps more discriminative. In experiments, we demonstrate the accuracy and\ngeneralizability of our method in weakly supervised localization tasks on the\nMS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is\nfurther validated in the text-to-region association task. On the Flickr30k\nEntities dataset, we achieve promising performance in phrase localization by\nleveraging the top-down attention of a CNN model that has been trained on\nweakly labeled web images.","url_abs":"http://arxiv.org/abs/1608.00507v1","url_pdf":"http://arxiv.org/pdf/1608.00507v1.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":"top-down-neural-attention-by-excitation","repo_url":"https://github.com/greydanus/excitationbp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"top-down-neural-attention-by-excitation","repo_url":"https://github.com/lassiraa/weighting-game","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"top-down-neural-attention-by-excitation","repo_url":"https://github.com/stresearch/xfr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1608.00507","atlas_url":"https://app.syntology.ai/?focus=1608.00507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.00507"}},"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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