{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:38:38Z","timestamp":1723016318692},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Usually, it is difficult to determine the scale and aspect ratio of anchors for anchor-based object detection methods. Current state-of-the-art object detectors either determine anchor parameters according to objects' shape and scale in a dataset, or avoid this problem by utilizing anchor-free methods, however, the former scheme is dataset-specific and the latter methods could not get better performance than the former ones. In this paper, we propose a novel anchor augmentation method named AADI, which means Augmenting Anchors by the Detector Itself. AADI is not an anchor-free method, instead, it can convert the scale and aspect ratio of anchors from a continuous space to a discrete space, which greatly alleviates the problem of anchors' designation. Furthermore, AADI is a learning-based anchor augmentation method, but it does not add any parameters or hyper-parameters, which is beneficial for research and downstream tasks. Extensive experiments on COCO dataset demonstrate the effectiveness of AADI, specifically, AADI achieves significant performance boosts on many state-of-the-art object detectors (eg. at least +2.4 box AP on Faster R-CNN, +2.2 box AP on Mask R-CNN, and +0.9 box AP on Cascade Mask R-CNN). We hope that this simple and cost-efficient method can be widely used in object detection. Code and models are available at https:\/\/github.com\/WanXiaopei\/aadi.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/191","type":"proceedings-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T22:55:56Z","timestamp":1657925756000},"page":"1371-1377","source":"Crossref","is-referenced-by-count":0,"title":["Augmenting Anchors by the Detector Itself"],"prefix":"10.24963","author":[{"given":"Xiaopei","family":"Wan","sequence":"first","affiliation":[{"name":"Ant Group"}]},{"given":"Guoqiu","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Yujiu","family":"Yang","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Zhenhua","family":"Guo","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T07:08:15Z","timestamp":1658128095000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/191"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/191","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}