Cross Domain Residual Transfer Learning for Person Re-Identification

Furqan Khan, Francois Bremond
2019 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)  
This paper presents a novel way to transfer model weights from one domain to another using residual learning framework instead of direct fine-tuning. It also argues for hybrid models that use learned (deep) features and statistical metric learning for multi-shot person re-identification when training sets are small. This is in contrast to popular end-to-end neural network based models or models that use hand-crafted features with adaptive matching models (neural nets or statistical metrics).
more » ... experiments demonstrate that a hybrid model with residual transfer learning can yield significantly better re-identification performance than an end-to-end model when training set is small. On iLIDS-VID [42] and PRID [15] datasets, we achieve rank-1 recognition rates of 89.8% and 95%, respectively, which is a significant improvement over state-of-the-art.
doi:10.1109/wacv.2019.00219 dblp:conf/wacv/KhanB19 fatcat:ueeoky2fdjb3jh6l3nrku65ly4