Name
Title | ||
|---|---|---|
Deepfashion2: A Versatile Benchmark For Detection, Pose Estimation, Segmentation And Re-Identification Of Clothing Images |
Abstract | ||
|---|---|---|
Understanding fashion images has been advanced by benchmarks with rich annotations such as DeepFashion, whose labels include clothing categories, landmarks, and consumer-commercial image pairs. However, DeepFashion has nonnegligible issues such as single clothing-item per image, sparse landmarks (4 similar to 8 only), and no per-pixel masks, making it had significant gap from real-world scenarios. We fill in the gap by presenting DeepFashion2 to address these issues. It is a versatile benchmark of four tasks including clothes detection, pose estimation, segmentation, and retrieval. It has 801K clothing items where each item has rich annotations such as style, scale, view- point, occlusion, bounding box, dense landmarks (e.g. 39 for 'long sleeve outwear' and 15 for 'vest'), and masks. There are also 873K Commercial-Consumer clothes pairs. The annotations of DeepFashion2 are much larger than its counterparts such as 8x of FashionAI Global Challenge. A strong baseline is proposed, called Match R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner. Extensive evaluations are conducted with different criterions in Deep- Fashion2. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.00548 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
DocType | ISSN | Citations |
Conference | 1063-6919 | 7 |
PageRank | References | Authors |
0.42 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuying Ge | 1 | 13 | 1.83 |
| Ruimao Zhang | 2 | 325 | 18.86 |
| Xiaogang Wang | 3 | 9647 | 386.70 |
| Xiaoou Tang | 4 | 15728 | 670.19 |
| Ping Luo | 5 | 2540 | 111.68 |