Scalable Person Re-identification on Supervised Smoothed Manifold Song Bai, Xiang Bai, Qi Tian Huazhong University of Science and Technology University of Texas at San Antonio

Task Definition Search a probe in a gallery with the help of the training set Training set Y in pairwise constraints Testing set = Probe p + Gallery X Cam-A Probe Cam-B Gallery Remark: Pairwise-constrainted labels are different from category labels. 2018/4/12 Supervised Smoothed Manifold

Affinity learning in Person Re-identification Most Previous works (Feature + Metric Learning) Our contribution (Supervised Smoothed Manifold) Remark: a new direction which can be investigated. 2018/4/12 Supervised Smoothed Manifold

Motivation The illustration of the feature space True matching pairs A positive pair (labeled) An unlabeled pair A negative pair (labeled) True matching pairs False matching pairs 2018/4/12 Supervised Smoothed Manifold

Supervised Smoothed Manifold Solution On-line Construct the affinity graph with the gallery X and training set Y; Do affinity learning by propagating the pairwise-constrained labels; Return the manifold parameterized by the learning affinity. Off-line Input a given probe image; Do probe embedding on the manifold; Return the matching probabilities. More details can be found in the paper. 2018/4/12 Supervised Smoothed Manifold

Supervised Smoothed Manifold Advantage Three merits: Supervision: take advantage of the supervision in pairwise constraints, which is easily accessible in this task. Efficiency: highly-efficient on-line indexing. Generalization: a post-processing procedure (or a generic tool) to further boost the identification accuracies of most existing algorithms. 2018/4/12 Supervised Smoothed Manifold

Experimental Comparison Datasets: GRID, VIPeR, PRID450S, CUHK03 and Market-1501. Feature: LOMO, GOG, EFL6 and ResNet. Metric: Euclidean and XQDA. Boost the performances of various features and metrics. 2018/4/12 Supervised Smoothed Manifold

Experimental Comparison A new state-of-the-art on five benchmark datasets. Datasets R=1 R=5 R=10 R=20 mAP GRID 24.80 - 58.40 68.88 27.20 61.12 70.56 VIPeR 63.92 91.49 96.65 53.73 96.08 PRID450S 68.40 94.50 97.80 72.98 96.76 99.11 CUHK03 (labeled) 75.3 91.0 96.0 76.6 94.6 98.0 CUHK03 (detected) 65.5 88.4 94.8 72.7 92.4 96.1 Market-1501 76.04 48.45 88.18 76.18 Remark: for each dataset, the 1st row presents the best performances to date, and the 2nd row presents our results. 2018/4/12 Supervised Smoothed Manifold

Supervised Smoothed Manifold Conclusion Addresses person re-identification using affinity learning (or re-ranking, post-ranking), which did not receive enough attention in this field. A simple yet effective tool for various existing algorithms to further improve their performances. 2018/4/12 Supervised Smoothed Manifold

Thanks！ Codes are available at https://sites.google.com/site/songbaihust/.