{"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/end-to-end-saliency-mapping-via-probability","title":"End-to-End Saliency Mapping via Probability Distribution Prediction","arxiv_id":"1804.01793","date":"2018-04-05","proceeding":"CVPR 2016 6","authors":["Saumya Jetley","Naila Murray","Eleonora Vig"],"abstract":"Most saliency estimation methods aim to explicitly model low-level\nconspicuity cues such as edges or blobs and may additionally incorporate\ntop-down cues using face or text detection. Data-driven methods for training\nsaliency models using eye-fixation data are increasingly popular, particularly\nwith the introduction of large-scale datasets and deep architectures. However,\ncurrent methods in this latter paradigm use loss functions designed for\nclassification or regression tasks whereas saliency estimation is evaluated on\ntopographical maps. In this work, we introduce a new saliency map model which\nformulates a map as a generalized Bernoulli distribution. We then train a deep\narchitecture to predict such maps using novel loss functions which pair the\nsoftmax activation function with measures designed to compute distances between\nprobability distributions. We show in extensive experiments the effectiveness\nof such loss functions over standard ones on four public benchmark datasets,\nand demonstrate improved performance over state-of-the-art saliency methods.","url_abs":"http://arxiv.org/abs/1804.01793v1","url_pdf":"http://arxiv.org/pdf/1804.01793v1.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":"end-to-end-saliency-mapping-via-probability","repo_url":"https://github.com/saumya-jetley/cd_Saliency_PDP","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"caffe2","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"saliency-prediction","task_name":"Saliency Prediction"},{"task_slug":"text-detection","task_name":"Text Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.01793","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}