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-2.3
Paper Title FAST: FEATURE AGGREGATION FOR DETECTING SALIENT OBJECT IN REAL-TIME
Authors Lv Tang, Nanjing University, China; Bo Li, Youtu Lab,Tencent, China; Yanliang Wu, Bo Xiao, Southwest Jiaotong University, China; Shouhong Ding, Youtu Lab,Tencent, China
SessionIVMSP-2: Object Detection 2
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: [IVARS] Image & Video Analysis, Synthesis, and Retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper introduces a method named FAST for real-time salient object detection with an extremely efficient CNN architecture. Our proposed network starts from a single lightweight backbone and aggregates discriminative features through network-level and phase-level respectively. Based on the multi-scale feature propagation, FAST substantially reduces the number of parameters, but still obtains sufficient receptive field and enhances the model learning ability, which strikes a balance between the speed and performance. To better preserve object boundaries, we also explore the complementary between salient object information and edge information within our lightweight architecture. Extensive evaluations and analysis demonstrate that the proposed algorithm achieves the leading accuracy performance with real-time speed (186fps) which is significantly faster than the existing state-of-the-art methods.