G-Head: Gating Head for Multi-task Learning in One-stage Object Detection
文献类型：会议论文
| 作者 | He, Jiang1,2 ; Qingyi, Gu1
|
| 出版日期 | 2022-03 |
| 会议日期 | 2022-7 |
| 会议地点 | Taiwan |
| 英文摘要 | 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. |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48641]  |
| 专题 | 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 |
| 通讯作者 | Qingyi, Gu |
| 作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | He, Jiang,Qingyi, Gu. G-Head: Gating Head for Multi-task Learning in One-stage Object Detection[C]. 见:. Taiwan. 2022-7. |
入库方式： OAI收割
来源：自动化研究所
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