{"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/modeling-bottom-up-and-top-down-attention","title":"Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1","arxiv_id":"1904.02741","date":"2019-11-18","proceeding":null,"authors":[],"abstract":"Previous studies suggested that lateral interactions of V1 cells are\nresponsible, among other visual effects, of bottom-up visual attention\n(alternatively named visual salience or saliency). Our objective is to mimic\nthese connections with a neurodynamic network of firing-rate neurons in order\nto predict visual attention. Early visual subcortical processes (i.e. retinal\nand thalamic) are functionally simulated. An implementation of the cortical\nmagnification function is included to define the retinotopical projections\ntowards V1, processing neuronal activity for each distinct view during scene\nobservation. Novel computational definitions of top-down inhibition (in terms\nof inhibition of return and selection mechanisms), are also proposed to predict\nattention in Free-Viewing and Visual Search tasks. Results show that our model\noutpeforms other biologically-inpired models of saliency prediction while\npredicting visual saccade sequences with the same model. We also show how\ntemporal and spatial characteristics of inhibition of return can improve\nprediction of saccades, as well as how distinct search strategies (in terms of\nfeature-selective or category-specific inhibition) can predict attention at\ndistinct image contexts.","url_abs":"http://arxiv.org/abs/1904.02741v3","url_pdf":"http://arxiv.org/pdf/1904.02741v3.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":"modeling-bottom-up-and-top-down-attention","repo_url":"https://github.com/dberga/NSWAM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"saliency-prediction","task_name":"Saliency Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}