Recently, panoramic imaging system has been attracting a lot of attention in various real-world applications due to its all-around sensing abilities. Despite the success of semantic segmentation, the performance of panoramic segmentation is still poor because the number of annotated panoramic datasets is insufficient and existing methods cannot handle the structural distortions in panoramic images caused by wide FoV. In this paper, we present a novel PAnoramic Segmentation Transformers (PASTs) trained by a knowledge distillation strategy with teacher-student branches. We first train the teacher using labeled pinhole images. The knowledge learned from the teacher is transferred to the student via feature distillation. To this end, we exploit the distorted pinhole images to force the attention and the prediction from the teacher consistent with those from the student. In addition, we adopt the entropy loss to train the student with unlabeled panoramic images. Experimental results demonstrate the effectiveness of our method, both qualitatively and quantitatively.
|Title of host publication
|2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
|IEEE Computer Society
|Number of pages
|Published - 2022
|29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
Duration: 2022 Oct 16 → 2022 Oct 19
|Proceedings - International Conference on Image Processing, ICIP
|29th IEEE International Conference on Image Processing, ICIP 2022
|22/10/16 → 22/10/19
Bibliographical notePublisher Copyright:
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing