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-23.4
Paper Title SEMANTIC-AWARE UNPAIRED IMAGE-TO-IMAGE TRANSLATION FOR URBAN SCENE IMAGES
Authors Zongyao Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan
SessionIVMSP-23: Applications 1
LocationGather.Town
Session Time:Thursday, 10 June, 15:30 - 16:15
Presentation Time:Thursday, 10 June, 15:30 - 16:15
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 Unpaired image-to-image (I2I) translation methods have been developed for several years. Present methods do not take into consideration semantic information of the original image, which may perform well on simple datasets of uncomplicated scenes, however, fail in complex datasets of scenes involving abundant objects, such as urban scenes. To tackle this problem, in this paper, we reasonably modify the previous problem setting and present a novel semantic-aware method. Specifically, in training, we use additional semantic label maps of training images, while in the test, no labels are required. We originally adopt a semantic knowledge distillation strategy to acquire semantic information from the labels and construct a particular normalization layer to introduce semantic information. Being aware of the pixel-level semantic information, our method can realize better I2I translation than the previous methods. Experiments are conducted on benchmark datasets of urban scenes to validate the effectiveness of our method.