Unsupervised Person Re-Identification via Deep Attribute Learning

Unsupervised domain adaptation (UDA) has emerged as an effective paradigm for reducing the huge manual annotation cost for Person Re-Identification (Re-ID). Many of the recent UDA methods for Re-ID are clustering-based and select all the pseudo-label samples in each iteration for the model training. However, there are many wrong labeled samples that will mislead the model optimization under this circumstance. To solve this problem, we propose a Progressive Unsupervised Domain Adaptation (PUDA) framework for image-based Person Re-ID to reduce the negative effect of wrong pseudo-label samples on the model training process. Specifically, we first pretrain a CNN model on a labeled source dataset, then finetune the model on unlabeled target dataset with the following three steps iteratively: 1) estimating pseudo-labels for all the images in the target dataset with the model trained in the last iteration; 2) extending the training set by adding pseudo-label samples with higher label confidence; 3) updating the CNN model with the expanded training set in a supervised manner. During the iteration process, the number of pseudo-label samples added increased progressively. In particular, a Moderate Initial Selections (MIS) strategy for pseudo-label sampling is also proposed to reduce the negative impacts of random noise features in the early iterations and mislabeled samples in the late iterations on the model. The proposed framework with MIS strategy is validated on the Duke-to-Market, Market-to-Duke unsupervised domain adaptation tasks and achieves improvements of 4.2 points (absolute, i.e., 80.0% vs. 75.8%) and 1.7 points (absolute, i.e., 70.7% vs. 69.0%) in mAP correspondingly.

Unsupervised Domain Adaptation (UDA) methods for person Re-Identification (Re-ID) rely on target domain samples to model the marginal distribution of the data. To deal with the lack of target domain labels, UDA methods leverage information from labeled source samples and unlabeled target samples. A promising approach relies on the use of unsupervised learning as part of the pipeline, such as clustering methods. The quality of the clusters clearly plays a major role in methods performance, but this point has been overlooked. In this work, we propose a multi-step pseudo-label refinement method to select the best possible clusters and keep improving them so that these clusters become closer to the class divisions without knowledge of the class labels. Our refinement method includes a cluster selection strategy and a camera-based normalization method which reduces the within-domain variations caused by the use of multiple cameras in person Re-ID. This allows our method to reach state-of-the-art UDA results on DukeMTMC-Market1501 (source-target). We surpass state-of-the-art for UDA Re-ID by 3.4% on Market1501-DukeMTMC datasets, which is a more challenging adaptation setup because the target domain (DukeMTMC) has eight distinct cameras. Furthermore, the camera-based normalization method causes a significant reduction in the number of iterations required for training convergence.

Person re-identification (ReID) is to identify the same person across non-overlapping camera views. After a decade of development, the methods based on deep networks have achieved high performance on benchmarks and become mainstream. In applications, the features of gallery images extracted by deep learning-based methods are stored to speed up the query process and protect the sensitive information contained in the images. Unfortunately, it is demonstrated that turning the images into features cannot properly protect privacy, as these features could be reversed to the corresponding images, revealing the sensitive information they contain. Therefore, for preventing privacy leakage, recent methods learn their features against some feature reversal methods, and most conventional reversal methods focus on minimizing the difference between a reconstruction and its original image. However, there could be many reasonable reconstruction results from a single feature, and the conventional reversal methods will inevitably generate reconstruction results that lie in a different distribution from one of the original images, which cannot properly assess the private information for learning to protect and thus hamper the privacy-protected feature learning. To mitigate this problem, we enforce the reconstructions to follow the same distribution as the original images by the generative adversarial network (GAN). We operate this GAN-based feature reversal module accompanied by the conventional ReID feature extraction module and form a novel GAN-based feature privacy-protected person ReID model, which is expected to protect feature privacy so as against reversal attack and maintain ReID utility. We demonstrate that optimizing ReID model to accommodate privacy protection faces a double adversarial objective and is thus challenging. As a remedy, we design a novel two-step training and lazy update strategy that alternatively optimizes the feature extraction module and stabilizes the update process of the GAN-based feature reversal module. To evaluate the efficiency of the model in balancing its ReID utility and feature privacy protection, we introduce a novel metric called utility-reversibility ratio (URR). Compared with existing privacy-protected feature extraction models, the proposed method achieves a better balance between privacy protection and person ReID performance. Extensive experiments validate that our model can effectively protect feature privacy at a tiny accuracy cost, and validate the effectiveness of our model with the emerging diffusion model.

Deep learning-based person re-identification methods: A survey and outlook of recent works

Person re-identification (Re-ID) is a research hot spot in the field of intelligent video analysis, and it is also a challenging task. As the number of samples grows larger, traditional metric and feature learning methods fall into bottleneck, while it just meets the needs of deep learning algorithm, which perform very well in person re-identification. Although they have achieved good results in the field of supervised learning, their application in real-world scenarios is not very satisfactory. This is mainly because in the real world, a huge number of labeled images are hard to obtain, and even if they are obtained, the cost is expensive. Meanwhile, the performance of deep learning in unsupervised metrics is not ideal. For solving the problem, we propose a new method based on unsupervised domain adaptation (UDA) and re-ranking, and name it UDA[Formula: see text]. As for this method, we first train a camera-aware style transfer model to gain camstyle images. Then we further reduce the difference between the domain of the target and source by using invariant feature, and further improve their commonality. In addition, re-ranking is also introduced to optimize the matching results. This method can not only reduce the cost of obtaining labeled data, but also improve the accuracy. Experimental results show that our method can outperform the most advanced method by 4% on Rank-1 and 14% on mAP. The results also better confirm the effectiveness of Re-ranking module and provide a new idea for domain adaptation by unsupervised methods in the future.

Source-free Style-diversity Adversarial Domain Adaptation with Privacy-preservation for person re-identification

Object detection in remote sensing images has been a topic of interest that has gradually gained attention over the years due to the wide variety of related applications. Even though there is an extensive number of methods developed for object detection, there are still several challenges that remain unsolved, such as visual appearance variations, occlusions, and background clutter. Satellite images reveal a texture problem; it is difficult to differentiate between the background and the object of interest. In order to overcome this problem and exploit more of the spectral features of images, Discrete Wavelet Transform (DWT) is embedded into one of the most superior methods for object detection, which is Faster Region-based Convolutional Network (FRCNN). The accuracy of FRCNN is boosted by introducing the wavelet decomposition. The performance of the proposed strategy is tested, evaluated, and compared to the original FRCNN using two different datasets.

Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries

Unsupervised generalizable multi-source person re-identification: A Domain-specific adaptive framework

Person reidentification (Re-ID) aims to match specific pedestrians across nonoverlapping camera views. Due to the dramatic disparities between different datasets, transferring a Re-ID model trained on the source domain to the target domain is challenging. Some outstanding unsupervised domain adaptation (UDA) Re-ID methods use clustering to generate pseudolabels, optimizing the model on the target domain, but the pseudolabels are inevitably noisy. To address the above issues, we propose a framework named mini-transformer with pooling (MTP) to facilitate the generation of superior quality pseudolabels by improving the model’s feature representation capability. First, we introduce an effective mini-transformer (MT) that can be placed directly behind the CNNs as a feature extractor to capture long-range dependency. Then, we design two delicate pooling methods named global hybrid pooling (GHP) and global subvalue pooling (GSVP) to suit mini-transformer’s tremendous capability without increasing computational complexity. Specifically, GHP can keep more global information and GSVP can keep more discriminative information. Finally, experiments on four mainstream UDA Re-ID tasks demonstrate that MTP achieves competitive mAP and rank-1 accuracy to the current state-of-the-art methods, suggesting that our technique is simple but effective. In addition to UDA Re-ID, our MTP can be extended to other supervised retrieval tasks.

Person re-identification (Re-ID) has advanced significantly through the integration of deep learning techniques, with face recognition serving as a foundational component. This study presents a comprehensive analysis of state-of-the-art approaches spanning face detection, alignment, recognition, and cross-camera person Re-ID. Deep Convolutional Neural Networks (CNNs), attention mechanisms, and generative models (GANs) drive progress in robust feature extraction, occlusion handling, and domain adaptation. Landmark techniques such as DeepFace, DeepID2+, and center loss have achieved near-human face verification accuracy, while cascaded CNNs and Kalman-filter-based tracking enhance detection and temporal consistency in video surveillance. Emerging trends include transformer-based models, multi-modal biometric fusion, and edge-cloud optimization for scalable deployment. However, challenges remain in cross-domain generalization, fairness, and full-body Re-ID integration. This synthesis identifies critical research gaps and underscores the need for holistic, real-time, and ethically sound Re-ID systems capable of operating under diverse real-world conditions.

For most unsupervised person re-identification (re-ID), people often adopt unsupervised domain adaptation (UDA) method. UDA often train on the labeled source dataset and evaluate on the target dataset, which often focuses on learning differences between the source dataset and the target dataset to improve the generalization of the model. Base on these, we explore how to make use of the similarity of samples to conduct a fully unsupervised method which just trains on the unlabeled target dataset. Concretely, we propose a hierarchical clustering-guided re-ID (HCR) method. We use hierarchical clustering to generate pseudo labels and use these pseudo labels as monitors to conduct the training. In order to exclude hard examples and promote the convergence of the model, We use PK sampling in each iteration, which randomly selects a fixed number of samples from each cluster for training. We evaluate our model on Market-1501, DukeMTMC-reID and MSMT17. Results show that HCR gets the state-of-the-arts and achieves 55.3% mAP on Market-1501 and 46.8% mAP on DukeMTMC-reID. Our code will be released soon.

Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts. However, classifying plant specimens based on image data alone is challenging: some species exhibit large variations in visual appearance, while at the same time different species are often visually similar; additionally, species observations follow a highly imbalanced, long-tailed distribution due to differences in abundance as well as observer biases. On the other hand, most species observations are accompanied by side information about the spatial, temporal and ecological context. Moreover, biological species are not an unordered list of classes but embedded in a hierarchical taxonomic structure. We propose a multimodal deep learning model that takes into account these additional cues in a unified framework. Our Digital Taxonomist is able to identify plant species in photographs better than a classifier trained on the image content alone, the performance gained is over 6 percent points in terms of accuracy.

Local features have been shown effective in supervised person re-identification (Re-ID). However, it is hard to obtain workable local features in an unsupervised situation. In this paper, we propose a novel method to learn discriminative multiple granularity features in unsupervised domain adaptation (UDA) Re-ID. Firstly, we propose Multiple Granularity Memory Dictionary Group (MGMDG) to learn discriminative local features and global features respectively by contrastive learning for features of each granularity with the corresponding memory dictionary. Secondly, we design a teacher-student framework in which the teacher model supervises the student model to learn the multiple granularity features with a soft triplet loss. Finally, we use the concatenation of multiple granularity features to generate better pseudo labels for local features. Extensive experimental results show that the performance of the proposed method on Market1501, DukeMTMC, MSMT17 datasets significantly outperforms the state-of-the-art UDA Re-ID methods by a large margin.

Unsupervised domain adaptation (UDA) aims to mitigate the domain shift that occurs when transferring knowledge from a labeled source domain to an unlabeled target domain. While it has been studied for application in unsupervised person re-identification (ReID), the relations of feature distribution across the source and target domains remain underexplored, as they either ignore the local relations or omit the in-depth consideration of negative transfer when two domains do not share identical label spaces. In light of the above, this paper presents an innovative part-aware progressive adaptation network (PPAN) that exploits global and local relations for UDA-based ReID across domains. A multi-branch network is developed that explicitly learns discriminative feature representation from both whole-body images and body-part images under the supervision of a labeled source domain. Within each network branch, an independent UDA constraint is designed that aligns the global and local feature distributions from a labeled source domain with those of an unlabeled target domain. In addition, a novel progressive adaptation strategy (PAS) is designed that effectively alleviates the negative influence of outlier source identities. The proposed unsupervised ReID model is evaluated on five widely used datasets (Market-1501, DukeMTMC-reID, CUHK03, VIPeR and PRID), and experimental results demonstrate its superior robustness and effectiveness relative to state-of-the-art approaches.