• Corpus ID: 244773138

Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification

  title={Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification},
  author={Zizheng Yang and Xin Jin and Kecheng Zheng and Feng Zhao},
Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID is more challenging and exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised representation learning with contrastive objectives, in this paper, we design an Unsupervised Pre-training framework for ReID based on the contrastive learning (CL) pipeline, dubbed UP… 


Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification
This work introduces an Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for simultaneously learning an attribute-semantic and identity-discriminative feature representation space transferrable to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain.
Style Normalization and Restitution for Generalizable Person Re-Identification
The aim of this paper is to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains, and to enforce a dual causal loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features.
Semantics-Aligned Representation Learning for Person Re-identification
A framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs is proposed and achieves the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID.
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification
A novel soft softmax-triplet loss is proposed to support learning with soft pseudo triplet labels for achieving the optimal domain adaptation performance in person re-identification models.
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
A novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter is proposed and significantly outperforms state-of-the-art methods on this dataset.
Joint Discriminative and Generative Learning for Person Re-Identification
This paper proposes a joint learning framework that couples re-id learning and data generation end-to-end and renders significant improvement over the baseline without using generated data, leading to the state-of-the-art performance on several benchmark datasets.
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification
Through domain adaptation experiment, it is shown that images generated by SPGAN are more suitable for domain adaptation and yield consistent and competitive re-ID accuracy on two large-scale datasets.
A Novel Unsupervised Camera-Aware Domain Adaptation Framework for Person Re-Identification
This paper highlights the presence of camera-level sub-domains as a unique characteristic in person Re-ID, and develops a “camera-aware” domain adaptation method via adversarial learning, and exploits the temporal continuity in each camera of target domain to create discriminative information.
A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification
The experimental results demonstrate that the bottom-up clustering approach to jointly optimize a convolutional neural network and the relationship among the individual samples is not only superior to state-of-the-art unsupervised re-ID approaches, but also performs favorably than competing transfer learning and semi-supervised learning methods.
Bag of Tricks and a Strong Baseline for Deep Person Re-Identification
A simple and efficient baseline for person re-identification with deep neural networks by combining effective training tricks together, which achieves 94.5% rank-1 and 85.9% mAP on Market1501 with only using global features.