Name
Abstract | ||
|---|---|---|
Learning discriminative, view-invariant and multi-scale representations of person appearance with different semantic levels is of paramount importance for person Re Identification (Re-ID). A surge of effort has been spent by the community to learn deep Re-ID models capturing a holistic single semantic level feature representation. To improve the achieved results, additional visual attributes and body part-driven models have been considered. However, these require extensive human annotation labor or demand additional computational efforts. We argue that a pyramid-inspired method capturing multi-scale information may overcome such requirements. Precisely, multi-scale stripes that represent visual information of a person can be used by a novel architecture factorizing them into latent discriminative factors at multiple semantic levels. A multi-task loss is combined with a curriculum learning strategy to learn a discriminative and invariant person representation which is exploited for triplet-similarity learning. Results on three benchmark Re-ID datasets demonstrate that better performance than existing methods are achieved (e.g., more than 90% accuracy on the Duke-MTMC dataset). |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPRW.2019.00196 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
Field | DocType | ISSN |
Computer vision,Computer science,Artificial intelligence | Conference | 2160-7508 |
Citations | PageRank | References |
2 | 0.35 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niki Martinel | 1 | 349 | 24.39 |
| Gian Luca Foresti | 2 | 44 | 7.06 |
| C. Micheloni | 3 | 934 | 62.52 |