2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-1.1
Paper Title MULTI-SCALE SAMPLE SELECTION BASED ON STATISTICAL CHARACTERISTICS FOR OBJECT DETECTION
Authors Zhiguo Li, Yuan Yuan, Dandan Ma, Northwestern Polytechnical University, China
SessionIVMSP-1: Object Detection 1
LocationGather.Town
Session Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In the domain of object detection, automatically selecting positive and negative samples methods have become a hot research topic in recent years. However, most of them focus on improving the sampling process but ignore the relationship between object size and feature map, in which the shallow and deep feature layers can capture small and large size objects well, respectively. In this paper, we propose a multi-scale sample selection based on statistical characteristics for object detection. To improve the robustness of the Intersection over Union (IoU) threshold, we design a multi-scale sample selection module (MSSM), which takes full advantage of different feature layers. Besides, we introduce a multi-scale attention module (MSAM) by embedding in the feature pyramid networks (FPN) to improve the efficiency of feature fusion. Experiments on MS COCO dataset demonstrate that our method achieves significant improvement over the state-of-the-art methods.