{"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/non-uniform-subset-selection-for-active-1","title":"Non-Uniform Subset Selection for Active Learning in Structured Data","arxiv_id":null,"date":"2017-06-01","proceeding":"Computer Vision and Pattern Recognition (CVPR) 2017 6","authors":["Sujoy Paul","Jawadul H. Bappy","Amit Roy-Chowdhury"],"abstract":"Several works have shown that relationships between data points (i.e., context) in structured data can be exploited\r\nto obtain better recognition performance. In this paper, we explore a different, but related, problem: how can these interrelationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling\r\neffort. Towards this goal, we propose an active learning framework to select an optimal subset of data points for manual labeling by exploiting the relationships between them. We construct a graph from the unlabeled data to represent\r\nthe underlying structure, such that each node represents a data point, and edges represent the inter-relationships between them. Thereafter, considering the flow of beliefs in this graph, we choose those samples for labeling which minimize the joint entropy of the nodes of the graph. This results in significant reduction in manual labeling effort without compromising recognition performance. Our method chooses non-uniform number of samples from each batch of streaming data depending on its information content. Also, the\r\nsubmodular property of our objective function makes it computationally efficient to optimize. The proposed framework\r\nis demonstrated in various applications, including document\r\nanalysis, scene-object recognition, and activity recognition.","url_abs":"https://intra.ece.ucr.edu/~supaul/Webpage_files/CVPR2017_1.pdf","url_pdf":"https://intra.ece.ucr.edu/~supaul/Webpage_files/CVPR2017_1.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":"non-uniform-subset-selection-for-active-1","repo_url":"https://github.com/sujoyp/context-active-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}