In order to learn object segmentation models in videos, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data is time-consuming and labor-intensive. In this paper, we exploit existing annotations in source images and transfer such visual information to segment videos with unseen object categories. Without using any annotations in the target video, we propose a method to jointly mine useful segments and learn feature representations that better adapt to the target frames. The entire process is decomposed into two tasks: (1) solving a submodular function for selecting object-like segments, and (2) learning a CNN model with a transferable module for adapting seen categories in the source domainÂ to the unseen target video. We present an iterative update scheme between two tasks to self-learn the final solution for object segmentation. Experimental results on numerous benchmark datasets show that the proposed method performs favorably against the state-of-the-art algorithms.
|Title of host publication||Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers|
|Editors||Hongdong Li, C.V. Jawahar, Greg Mori, Konrad Schindler|
|Number of pages||17|
|Publication status||Published - 2019|
|Event||14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia|
Duration: 2018 Dec 2 → 2018 Dec 6
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||14th Asian Conference on Computer Vision, ACCV 2018|
|Period||18/12/2 → 18/12/6|
Bibliographical noteFunding Information:
Acknowledgments. This work is supported in part by Ministry of Science and Technology under grants MOST 105-2221-E-001-030-MY2 and MOST 107-2628-E-001-005-MY3.
This work is supported in part by Ministry of Science and Technology under grants MOST 105-2221-E-001-030-MY2 and MOST 107-2628-E-001-005-MY3.
© 2019, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)