2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-IVASR-3.4
Paper Title APNET: ATTRIBUTE PARSING NETWORK FOR PERSON RE-IDENTIFICATION
Authors Chiat Pin Tay, Kim Hui Yap, Nanyang Technological University, Singapore
SessionMLR-APPL-IVASR-3: Machine learning for image and video analysis, synthesis, and retrieval 3
LocationArea E
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Most person re-identification methods rely solely on the pedestrian identity for learning. Person attributes, such as gender, clothing colors, carried bags, etc, are however seldom used. These attributes are highly identity-related and should be capitalized fully. Thus, we propose Attribute Parsing Network (APNet), an architecture designed for both image and person attribute learning and retrievals. To further enhance the re-id performance, we propose to leverage saliency maps and human parsing to boost the foreground features, which when trained together with the global and local networks, resulted in more generic and robust encoded representations. This proposed method achieved state-of-the-art accuracy performance on both Market1501 (87.3\% mAP and 95.2\% Rank1) and DukeMTMC-reID (78.8\% mAP and 89.2\% Rank 1) datasets.