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G-Head: Gating Head for Multi-task Learning in One-stage Object Detection
He, Jiang1,2; Qingyi, Gu1
2022-03
Conference Name2022 IEEE International Conference on Multimedia & Expo (ICME)
Conference Date2022-7
Conference PlaceTaiwan
Abstract
Object detection is commonly formulated as a multi-task learning problem in deep learning methods. Due to the divergence between classification and regression tasks, modern one-stage detectors typically utilize two parallel branches as the detection head, which might be sub-optimal. In this paper, we propose a new Gating Head (G-Head) to enhance the interaction between different tasks and promote the multi-task learning process. By introducing Multi-Scale Aggregation (MSA), Multi-Aspect Learning (MAL), and Gating Selector (GS), our method can signifificantly boost the performance of existing one-stage frameworks with fewer parameters and computational costs. To validate the effificiency, effectiveness, and generalization of our G-Head, extensive experiments are conducted on the challenging MS COCO dataset. Without bells and whistles, we achieve a new state-of-the-art 48.7 AP under single-model and single-scale test.
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48641
Collection精密感知与控制研究中心_精密感知与控制
中国科学院自动化研究所
Corresponding AuthorQingyi, Gu
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
He, Jiang,Qingyi, Gu. G-Head: Gating Head for Multi-task Learning in One-stage Object Detection[C],2022.
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