{"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/do-saliency-models-detect-odd-one-out-targets","title":"Do Saliency Models Detect Odd-One-Out Targets? New Datasets and Evaluations","arxiv_id":"2005.06583","date":"2020-05-13","proceeding":null,"authors":["Iuliia Kotseruba","Calden Wloka","Amir Rasouli","John K. Tsotsos"],"abstract":"Recent advances in the field of saliency have concentrated on fixation prediction, with benchmarks reaching saturation. However, there is an extensive body of works in psychology and neuroscience that describe aspects of human visual attention that might not be adequately captured by current approaches. Here, we investigate singleton detection, which can be thought of as a canonical example of salience. We introduce two novel datasets, one with psychophysical patterns and one with natural odd-one-out stimuli. Using these datasets we demonstrate through extensive experimentation that nearly all saliency algorithms do not adequately respond to singleton targets in synthetic and natural images. Furthermore, we investigate the effect of training state-of-the-art CNN-based saliency models on these types of stimuli and conclude that the additional training data does not lead to a significant improvement of their ability to find odd-one-out targets. Datasets are available at http://data.nvision2.eecs.yorku.ca/P3O3/.","url_abs":"https://arxiv.org/abs/2005.06583v3","url_pdf":"https://arxiv.org/pdf/2005.06583v3.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":"do-saliency-models-detect-odd-one-out-targets","repo_url":"https://github.com/ykotseruba/P3O3_metrics","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"do-saliency-models-detect-odd-one-out-targets","repo_url":"https://github.com/ykotseruba/SALICONtf","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"odd-one-out","task_name":"Odd One Out"}],"methods":[],"datasets_introduced":[{"slug":"o3","name":"O3","full_name":"Odd-One-Out Dataset"},{"slug":"p3","name":"P3","full_name":"Psychophysical Patterns Dataset"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2005.06583","atlas_url":"https://app.syntology.ai/?focus=2005.06583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.06583"}},"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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