Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial Samples

Abstract

In this paper, we aim to tackle the one-shot person re-identification problem where only one image is labelled for each person, while other images are unlabelled. This task is challenging due to the lack of sufficient labelled training data. To tackle this problem, we propose to iteratively guess pseudo labels for the unlabeled image samples, which are later used to update the re-identification model together with the labelled samples. A new sampling mechanism is designed to select unlabeled samples to pseudo labelled samples based on the distance matrix, and to form a training triplet batch including both labelled samples and pseudo labelled samples. We also design an HSoften-Triplet-Loss to soften the negative impact of the incorrect pseudo label, considering the unreliable nature of pseudo labelled samples. Finally, we deploy an adversarial learning method to expand the image samples to different camera views. Our experiments show that our framework achieves a new state-of-the-art one-shot Re-ID performance on Market-1501 (mAP 42.7%) and DukeMTMC-Reid dataset (mAP 40.3%). Code will be available soon.

References in corpus (10)

• Improving Person Re-identification by Attribute and Identity Learning
• FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
• Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
• Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
• Unsupervised Person Re-identification by Soft Multilabel Learning
• Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
• Style Aggregated Network for Facial Landmark Detection
• Let Features Decide for Themselves: Feature Mask Network for Person Re-identification
• Unsupervised Tracklet Person Re-Identification
• End-to-End Training of CNN Ensembles for Person Re-Identification