{"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/visual-semantic-navigation-using-scene-priors","title":"Visual Semantic Navigation using Scene Priors","arxiv_id":"1810.06543","date":"2018-10-15","proceeding":"ICLR 2019 5","authors":["Wei Yang","Xiaolong Wang","Ali Farhadi","Abhinav Gupta","Roozbeh Mottaghi"],"abstract":"How do humans navigate to target objects in novel scenes? Do we use the\nsemantic/functional priors we have built over years to efficiently search and\nnavigate? For example, to search for mugs, we search cabinets near the coffee\nmachine and for fruits we try the fridge. In this work, we focus on\nincorporating semantic priors in the task of semantic navigation. We propose to\nuse Graph Convolutional Networks for incorporating the prior knowledge into a\ndeep reinforcement learning framework. The agent uses the features from the\nknowledge graph to predict the actions. For evaluation, we use the AI2-THOR\nframework. Our experiments show how semantic knowledge improves performance\nsignificantly. More importantly, we show improvement in generalization to\nunseen scenes and/or objects. The supplementary video can be accessed at the\nfollowing link: https://youtu.be/otKjuO805dE .","url_abs":"http://arxiv.org/abs/1810.06543v1","url_pdf":"http://arxiv.org/pdf/1810.06543v1.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":"visual-semantic-navigation-using-scene-priors","repo_url":"https://github.com/barmayo/spatial_attention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[{"method_slug":"graph-convolutional-networks","method_name":"Graph Convolutional Networks"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.06543","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}