Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial Samples
This work addresses the challenge of limited labeled data in person re-identification for surveillance applications, representing an incremental improvement over existing methods.
The paper tackles the one-shot person re-identification problem by proposing an iterative framework that mines pseudo labels from unlabeled data, uses a novel sampling mechanism and softened triplet loss, and incorporates adversarial learning for view expansion, achieving state-of-the-art mAP scores of 42.7% on Market-1501 and 40.3% on DukeMTMC-Reid.
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.