Progressive Sparse Local Attention for Video Object Detection | |
Guo, Chaoxu1,2; Fan, Bin1; Gu, Jie1,2; Zhang, Qian3; Xiang, Shiming1,2; Prinet, Veronique1; Pan, Chunhong1 | |
2019-10 | |
会议日期 | 2019-10-27 |
会议地点 | Seoul, Korea |
期号 | 2019 |
页码 | 3909-3918 |
英文摘要 | Transferring image-based object detectors to the domain of videos remains a challenging problem. Previous efforts mostly exploit optical flow to propagate features across frames, aiming to achieve a good trade-off between accuracy and efficiency. However, introducing an extra model to estimate optical flow can significantly increase the overall model size. The gap between optical flow and high-level features can also hinder it from establishing spatial correspondence accurately. Instead of relying on optical flow, this paper proposes a novel module called Progressive Sparse Local Attention (PSLA), which establishes the spatial correspondence between features across frames in a local region with progressively sparser stride and uses the correspondence to propagate features. Based on PSLA, Recursive Feature Updating (RFU) and Dense Feature Transforming (DenseFT) are proposed to model temporal appearance and enrich feature representation respectively in a novel video object detection framework. Experiments on ImageNet VID show that our method achieves the best accuracy compared to existing methods with smaller model size and acceptable runtime speed. |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[91646207] ; Beijing Natural Science Foundation[L172053] ; Young Elite Scientists Sponsorship Program by CAST[2018QNRC001] ; National Science Foundation of China[61573352,61876180] |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/39103] |
专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
通讯作者 | Fan, Bin |
作者单位 | 1.Institute of Automation, Chinese Academy of Science 2.School of Artifical Intelligence, University of Chinese Academy of Science 3.Horizon Robotics |
推荐引用方式 GB/T 7714 | Guo, Chaoxu,Fan, Bin,Gu, Jie,et al. Progressive Sparse Local Attention for Video Object Detection[C]. 见:. Seoul, Korea. 2019-10-27. |
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