{"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/visualization-of-convolutional-neural","title":"Visualization of Convolutional Neural Networks for Monocular Depth Estimation","arxiv_id":"1904.03380","date":"2019-04-06","proceeding":"ICCV 2019 10","authors":["Junjie Hu","Yan Zhang","Takayuki Okatani"],"abstract":"Recently, convolutional neural networks (CNNs) have shown great success on\nthe task of monocular depth estimation. A fundamental yet unanswered question\nis: how CNNs can infer depth from a single image. Toward answering this\nquestion, we consider visualization of inference of a CNN by identifying\nrelevant pixels of an input image to depth estimation. We formulate it as an\noptimization problem of identifying the smallest number of image pixels from\nwhich the CNN can estimate a depth map with the minimum difference from the\nestimate from the entire image. To cope with a difficulty with optimization\nthrough a deep CNN, we propose to use another network to predict those relevant\nimage pixels in a forward computation. In our experiments, we first show the\neffectiveness of this approach, and then apply it to different depth estimation\nnetworks on indoor and outdoor scene datasets. The results provide several\nfindings that help exploration of the above question.","url_abs":"http://arxiv.org/abs/1904.03380v1","url_pdf":"http://arxiv.org/pdf/1904.03380v1.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":"visualization-of-convolutional-neural","repo_url":"https://github.com/JunjH/Visualizing-CNNs-for-monocular-depth-estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"interpretable-machine-learning","task_name":"Interpretable Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.03380","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}