Re-ID

Space-Time Guided Association Learning For Unsupervised Person Re-Identification

Person re-identification (Re-ID) aims to match images of the same person across distinct camera views. In this paper, we propose the Space-Time Guided Association Learning (STGAL) for unsupervised Re-ID without ground truth identity nor image …

Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network

Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are sometimes subtle. While several studies incorporate spatial-attention …

Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification

Video-based person re-identification (Re-ID) aims at matching video sequences of pedestrians across non-overlapping cameras. It is a practical yet challenging task of how to embed spatial and temporal information of a video into its feature …

Supervised Joint Domain Learning for Vehicle Re-Identification

in conjunction with 2019 NVIDIA AI City Challenge

Vehicle Re-identification with the Space-Time Prior

Vehicle re-identification (Re-ID) is fundamentally challenging due to the difficulties in data labeling, visual domain mismatch between datasets and diverse appearance of the same vehicle. We propose the adaptive feature learning technique based on …