{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:31:25Z","timestamp":1776889885861,"version":"3.51.2"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Object detection often struggles when applied to low-resource, domain-specific datasets. This challenge is exacerbated when dealing with sports-related data such as rugby, where fast-paced gameplay and tackles result in frequent instances of motion blur and occlusion, representing a substantial domain-shift from widely available pre-trained models. Given the high cost of manual labelling, we seek to determine whether we can minimise the number examples needed for fine-tuning by identifying implausible label classifications made by pre-trained object detection models. We do this using a coarse-grained labelling approach in the absence of detailed ground truth bounding boxes, allowing us to determine whether a label is implausible within the context of a rugby pitch. This is done to maximize the information provided by each example used for fine-tuning with the goal of minimizing the number of examples needed. Our results show that using pool-based, single-step uncertainty sampling to select examples from a subset of frames with implausible labels improves the model performance. More specifically, we show that fine-tuning on frames with the lowest confidence scores first can lead to greater performance after roughly 30 examples.<\/jats:p>","DOI":"10.3233\/faia241066","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T14:01:55Z","timestamp":1729173715000},"source":"Crossref","is-referenced-by-count":2,"title":["Frisbees and Dogs: Domain Adaptation for Object Detection with Limited Labels in Rugby Data"],"prefix":"10.3233","author":[{"given":"Will","family":"Connors","sequence":"first","affiliation":[{"name":"SFI Lero @ School of Computer Science and Statistics, Trinity College Dublin, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5869-5333","authenticated-orcid":false,"given":"Ellen","family":"Rushe","sequence":"additional","affiliation":[{"name":"School of Computing, Dublin City University, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2064-1238","authenticated-orcid":false,"given":"Anthony","family":"Ventresque","sequence":"additional","affiliation":[{"name":"SFI Lero @ School of Computer Science and Statistics, Trinity College Dublin, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241066","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T14:01:56Z","timestamp":1729173716000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241066","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}