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We\npropose an architecture which, instead, combines features extracted at\ndifferent levels of a Convolutional Neural Network (CNN). Our model is composed\nof three main blocks: a feature extraction CNN, a feature encoding network,\nthat weights low and high level feature maps, and a prior learning network. We\ncompare our solution with state of the art saliency models on two public\nbenchmarks datasets. Results show that our model outperforms under all\nevaluation metrics on the SALICON dataset, which is currently the largest\npublic dataset for saliency prediction, and achieves competitive results on the\nMIT300 benchmark.","url_abs":"http://arxiv.org/abs/1609.01064v2","url_pdf":"http://arxiv.org/pdf/1609.01064v2.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":"a-deep-multi-level-network-for-saliency","repo_url":"https://github.com/marcellacornia/mlnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-deep-multi-level-network-for-saliency","repo_url":"https://github.com/Amrit-pal-Singh/Saliency-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"saliency-prediction","task_name":"Saliency Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.01064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.01064"}},"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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