Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification

Abstract

Person re-identification (ReID) is an important task in computer vision. Recently, deep learning with a metric learning loss has become a common framework for ReID. In this paper, we also propose a new metric learning loss with hard sample mining called margin smaple mining loss (MSML) which can achieve better accuracy compared with other metric learning losses, such as triplet loss. In experi- ments, our proposed methods outperforms most of the state-of-the-art algorithms on Market1501, MARS, CUHK03 and CUHK-SYSU.

6 pages

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Cited by in corpus (14)

• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
• Batch DropBlock Network for Person Re-identification and Beyond
• In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation
• STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification
• Attribute-aware Identity-hard Triplet Loss for Video-based Person Re-identification
• Grafted network for person re-identification
• A unified neural network for object detection, multiple object tracking and vehicle re-identification
• Locally Adaptive Learning Loss for Semantic Image Segmentation
• Angular Triplet Loss-based Camera Network for ReID
• Global-Local Dynamic Feature Alignment Network for Person Re-Identification
• SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
• Improving Image co-segmentation via Deep Metric Learning
• Learning Quintuplet Loss for Large-scale Visual Geo-Localization
• Cross-Resolution Person Re-identification with Deep Antithetical Learning