{"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/a-review-on-deep-learning-techniques-applied","title":"A Review on Deep Learning Techniques Applied to Semantic Segmentation","arxiv_id":"1704.06857","date":"2017-04-22","proceeding":null,"authors":["Alberto Garcia-Garcia","Sergio Orts-Escolano","Sergiu Oprea","Victor Villena-Martinez","Jose Garcia-Rodriguez"],"abstract":"Image semantic segmentation is more and more being of interest for computer\nvision and machine learning researchers. Many applications on the rise need\naccurate and efficient segmentation mechanisms: autonomous driving, indoor\nnavigation, and even virtual or augmented reality systems to name a few. This\ndemand coincides with the rise of deep learning approaches in almost every\nfield or application target related to computer vision, including semantic\nsegmentation or scene understanding. This paper provides a review on deep\nlearning methods for semantic segmentation applied to various application\nareas. Firstly, we describe the terminology of this field as well as mandatory\nbackground concepts. Next, the main datasets and challenges are exposed to help\nresearchers decide which are the ones that best suit their needs and their\ntargets. Then, existing methods are reviewed, highlighting their contributions\nand their significance in the field. Finally, quantitative results are given\nfor the described methods and the datasets in which they were evaluated,\nfollowing up with a discussion of the results. At last, we point out a set of\npromising future works and draw our own conclusions about the state of the art\nof semantic segmentation using deep learning techniques.","url_abs":"http://arxiv.org/abs/1704.06857v1","url_pdf":"http://arxiv.org/pdf/1704.06857v1.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":"a-review-on-deep-learning-techniques-applied","repo_url":"https://github.com/Lxrd-AJ/Advanced_ML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-review-on-deep-learning-techniques-applied","repo_url":"https://github.com/demul/image_segmentation_project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.06857","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}