Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large …