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In this paper\nwe go beyond standard approaches to saliency prediction, in which gaze maps are\ncomputed with a feed-forward network, and present a novel model which can\npredict accurate saliency maps by incorporating neural attentive mechanisms.\nThe core of our solution is a Convolutional LSTM that focuses on the most\nsalient regions of the input image to iteratively refine the predicted saliency\nmap. Additionally, to tackle the center bias typical of human eye fixations,\nour model can learn a set of prior maps generated with Gaussian functions. We\nshow, through an extensive evaluation, that the proposed architecture\noutperforms the current state of the art on public saliency prediction\ndatasets. We further study the contribution of each key component to\ndemonstrate their robustness on different scenarios.","url_abs":"http://arxiv.org/abs/1611.09571v4","url_pdf":"http://arxiv.org/pdf/1611.09571v4.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":"predicting-human-eye-fixations-via-an-lstm","repo_url":"https://github.com/marcellacornia/sam","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"predicting-human-eye-fixations-via-an-lstm","repo_url":"https://github.com/chenxy99/ANOC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"saliency-prediction","task_name":"Saliency Prediction"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1611.09571","atlas_url":"https://app.syntology.ai/?focus=1611.09571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.09571"}},"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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