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-17.3
Paper Title AN ADAPTIVE PART-BASED MODEL FOR PERSON RE-IDENTIFICATION
Authors Xipeng Lin, Yubin Yang, Nanjing University, China
SessionIVMSP-17: Looking at People
LocationGather.Town
Session Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVSMR] Image & Video Sensing, Modeling, and Representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Existing part-based models for person Re-IDentification(Re-ID) usually suffer from part-misalignment problem caused by uniform partition of feature maps. The performances of part-based model are highly dependent on the semantically-aligned parts of the query and gallery images. However, misalignments occur very commonly in person Re-ID tasks due to the variations of viewpoints and object distances. To address the part-misalignment problem and learn a more discriminative embedding for person Re-ID, we propose a novel Adaptive Part-based Model (APM), which adaptively partition the extracted feature maps by a partition-aware module to learn an embedding. The proposed adaptive partition method is very robust to the variations of the pedestrian scale and effective in resolving the part-misalignment problem. Experimental results on three commonly used datasets, including Market-1501, DukeMTMC-reID and CUHK03, clearly demonstrate that the proposed method achieves the state-of-the-art performance.