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Cross Domain Residual Transfer Learning for Person Re-Identification
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).
doi:10.1109/wacv.2019.00219
dblp:conf/wacv/KhanB19
fatcat:ueeoky2fdjb3jh6l3nrku65ly4