Towards Fewer Labels: Support Pair Active Learning for Person Re-identification
Supervised-learning based person re-identification (re-id) require a large amount of manual labeled data, which is not applicable in practical re-id deployment. In this work, we propose a Support Pair Active Learning (SPAL) framework to lower the manual labeling cost for large-scale person reidentification. The support pairs can provide the most informative relationships and support the discriminative feature learning. Specifically, we firstly design a dual uncertainty selection strategy to iteratively discover support pairs and require human annotations. Afterwards, we introduce a constrained clustering algorithm to propagate the relationships of labeled support pairs to other unlabeled samples. Moreover, a hybrid learning strategy consisting of an unsupervised contrastive loss and a supervised support pair loss is proposed to learn the discriminative re-id feature representation. The proposed overall framework can effectively lower the labeling cost by mining and leveraging the critical support pairs. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art active learning methods on large-scale person re-id benchmarks.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningConstrained ClusteringPerson Re-IdentificationSimilar Papers 제목 키워드 기반
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings
Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models. However, in settings where personalization is necessary at deployment time …
Active Learningreinforcement-learningReinforcement LearningDeep Active Learning for Video-based Person Re-identification
It is prohibitively expensive to annotate a large-scale video-based person re-identification (re-ID) dataset, which makes fully supervised methods inapplicable to real-world deployment. How to maximally reduce the annota…
Active LearningPerson Re-IdentificationVideo-Based Person Re-IdentificationOne-Bit Active Query With Contrastive Pairs
How to achieve better results with fewer labeling costs remains a challenging task. In this paper, we present a new active learning framework, which for the first time incorporates contrastive learning into recently …
Active LearningContrastive LearningHow to Purchase Labels? A Cost-Effective Approach Using Active Learning Markets
We introduce and analyse active learning markets as a way to purchase labels, in situations where analysts aim to acquire additional data to improve model fitting, or to better train models for predictive analytics appli…
Active LearningNear-Polynomially Competitive Active Logistic Regression
We address the problem of active logistic regression in the realizable setting. It is well known that active learning can require exponentially fewer label queries compared to passive learning, in some cases using $\log …
Active Learningregression