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{ "pk": 29708, "title": "A Simple Computational Model of Salience Map Formation in the Brain", "subtitle": null, "abstract": "Many convolutional neural network (CNN)-based approaches are excellent functional models of visual attention, but lackcognitive and biological interpretations. In this work, I offer novel, cross-disciplinary justification for the Deep Gaze 1model, which calculates salience as a weighted average of feature maps from a pre-trained CNN. In the cognitive realm,experiments demonstrate that visual attention depends on multiple levels of real-world features (edges, text, faces). Thisis well-modeled using features from a naturalistically-trained CNN. Furthermore, neuroscience research strongly suggeststhat visual attention is computed in the superior colliculus, using information from multiple levels of the ventral visualstream; all information flow in Deep Gaze follows analogous pathways. To encourage broader adoption of this model,whose source code remains unpublished, I offer a readable implementation with minor changes for biological plausibility.It is validated on the MIT1003 dataset using features from MobileNetV2, with results comparable to the original DeepGaze.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [], "section": "Poster Session 1", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/9gn5c3gn", "frozenauthors": [ { "first_name": "Abe", "middle_name": "", "last_name": "Leite", "name_suffix": "", "institution": "Indiana University", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2020-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/29708/galley/19565/download/" } ] }