Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID. At first, we propose a multi-domain learning method to joint the real-world and synthetic data to train the model. Then, we propose the Identity Mining method to automatically generate pseudo labels for a part of the testing data, which is better than the k-means clustering. The tracklet-level re-ranking strategy with weighted features is also used to post-process the results. Finally, with multiple-model ensemble, our method achieves 0.7322 in the mAP score which yields third place in the competition. The codes are available at https://github.com/heshuting555/AICITY2020_DMT_VehicleReID.
Code (2)
Tasks
ClusteringRe-RankingVehicle Re-IdentificationSimilar Papers 제목 키워드 기반
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification
Vehicle re-identification (reID) is to identify a target vehicle in different cameras with non-overlapping views. When deploy the well-trained model to a new dataset directly, there is a severe performance drop because o…
Domain AdaptationGenerative Adversarial NetworkImage-to-Image TranslationTransfer Learning+3Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification
Recently, vehicle re-identification methods based on deep learning constitute remarkable achievement. However, this achievement requires large-scale and well-annotated datasets. In constructing the dataset, assigning glo…
Contrastive LearningDomain AdaptationUnsupervised Vehicle Re-IdentificationVehicle Re-IdentificationDCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification
Vehicle re-identification (Re-ID) has become a popular research topic owing to its practicability in intelligent transportation systems. Vehicle Re-ID suffers the numerous challenges caused by drastic variation in illumi…
TripletVehicle Re-IdentificationMultimodality Adaptive Transformer and Mutual Learning for Unsupervised Domain Adaptation Vehicle Re-Identification
Unsupervised Domain Adaptation Vehicle Re-Identification (UDA vehicle re-ID) aims to enable the model trained in the source domain dataset to adapt to the target domain data and obtain accurate re-identification results,…
AttributeDomain AdaptationPseudo LabelUnsupervised Domain Adaptation+1Cross Domain Knowledge Learning with Dual-branch Adversarial Network for Vehicle Re-identification
The widespread popularization of vehicles has facilitated all people's life during the last decades. However, the emergence of a large number of vehicles poses the critical but challenging problem of vehicle re-identific…
Domain AdaptationImage-to-Image TranslationVehicle Re-Identification