期刊
IEEE SIGNAL PROCESSING LETTERS
卷 27, 期 -, 页码 1864-1868出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2020.3029958
关键词
Convolutional neural networks; deep learning; object detection; video analysis; weakly supervised learning
资金
- NTU [04INS000338C130]
- MOE [RG28/18, RG22/19]
In weakly supervised object detection, most existing approaches are proposed for images. Without box-level annotations, these methods cannot accurately locate objects. Considering an object may show different motion from its surrounding objects or background, we leverage motion information to improve the detection accuracy. However, the motion pattern of an object is complex. Different parts of an object may have different motion patterns, which poses challenges in exploring motion information for object localization. Directly using motion information may degrade the localization performance. To overcome these issues, we propose a Motion Context Network (MC-Net) in this letter. Our method generates motion context features by exploiting neighborhood motion correlation information on moving regions. These motion context features are then incorporated with image information to improve the detection accuracy. Furthermore, we propose a temporal aggregation module, which aggregates features across frames to enhance the feature representation at the current frame. Experiments are carried out on ImageNet VID, which shows that our MC-Net significantly improves the performance of the image based baseline method (37.4% mAP v.s. 29.8% mAP).
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