Name
Abstract | ||
|---|---|---|
Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications. As the first step to understand this media, cartoon face recognition is a crucial but less-explored task with few datasets proposed. In this work, we first present a new challenging benchmark dataset, consisting of 389,678 images of 5,013 cartoon characters annotated with identity, bounding box, pose, and other auxiliary attributes. The dataset, named iCartoonFace, is currently the largest-scale, high-quality, rich-annotated, and spanning multiple occurrences in the field of image recognition, including near-duplications, occlusions, and appearance changes. In addition, we provide two types of annotations for cartoon media, i.e., face recognition, and face detection, with the help of a semi-automatic labeling algorithm. To further investigate this challenging dataset, we propose a multi-task domain adaptation approach that jointly utilizes the human and cartoon domain knowledge with three discriminative regularizations. We hence perform a benchmark analysis of the proposed dataset and verify the superiority of the proposed approach in the cartoon face recognition task. The dataset is available at https://iqiyi.cn/icartoonface.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3394171.3413726 | MM '20: The 28th ACM International Conference on Multimedia
Seattle
WA
USA
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7988-5 | 0 |
PageRank | References | Authors |
0.34 | 9 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zheng | 1 | 1 | 1.04 |
| Y. Zhao | 2 | 277 | 33.44 |
| Mengyuan Ren | 3 | 0 | 1.01 |
| He Yan | 4 | 1 | 1.04 |
| Xiangju Lu | 5 | 77 | 4.43 |
| Junhui Liu | 6 | 0 | 2.03 |
| Jia Li | 7 | 524 | 42.09 |