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
Login Paper Search My Schedule Paper Index Help

My ICASSP 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 IDIVMSP-34.1
Paper Title SEMANTIC-AWARE CONTEXT AGGREGATION FOR IMAGE INPAINTING
Authors Zhilin Huang, Chujun Qin, Ruixin Liu, Zhenyu Weng, Yuesheng Zhu, Peking University, China
SessionIVMSP-34: Inpaiting and Occlusions Handling
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
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 Recent attention-based image inpainting methods have made inspiring progress by propagating distant contextual information into holes. However, they tend to generate blurry contents since the propagation process is always misled by preliminarily-recovered holes features which are not well-inferred. To handle this problem, we propose a novel semantic-aware context aggregation module (SACA) that aggregates distant contextual information from a semantic perspective by exploiting the internal semantic similarity of the input feature map. Compared with existing attention mechanisms that model the relation of all pixel-pairs, SACA can suppress the impact of misleading holes features in context aggregation and significantly reduce computation burden by learning the relation between pixels and semantics. Also, we apply SACA to both high-level and low-level feature maps in our model for generating both semantically and visually plausible results. Extensive experiments on Outdoor Scenes, CelebA and Paris StreetView datasets validate the superiority of our method compared with existing methods.