Single-camera-training person reidentification (SCT re-ID) aims to train a reidentification (re-ID) model using single-camera-training (SCT) datasets where each person appears in only one camera. The main challenge of SCT re-ID is to learn camera-invariant feature representations without cross-camera same-person (CCSP) data as supervision. Previous methods address it by assuming that the most similar person should be found in another camera. However, this assumption is not guaranteed to be correct. In this article, we propose a novel solution: the camera-invariant meta-learning network (CIMN) for SCT re-ID. CIMN operates under the premise that camera-invariant feature representations should remain robust despite changes in camera settings. To achieve this, we partition the training data into a meta-train set and a meta-test set based on camera IDs. We then conduct a cross-camera simulation (CCS) using a meta-learning strategy, aiming to enforce the feature representations learned from the meta-train set to be robust when applied to the meta-test set. We further introduce three specific loss functions to leverage potential identity relations between the meta-train set and the meta-test set. Through the CCS and the introduced loss functions, CIMN can extract feature representations that are both camera-invariant and identity-discriminative even in the absence of CCSP data. Our experimental results demonstrate that CIMN can extract feature representations that are both camera-invariant and identity-discriminative, even in the absence of CCSP data. our method achieves comparable performance with and without the use of CCSP data, and outperforms state-of-the-art methods on three SCT re-ID benchmarks.

Conventional Multi-Source Free Domain Adaptation (MSFDA) assumes that each source domain provides a single source model, and all source models adopt a uniform architecture. This paper introduces Zoo-MSFDA, a more general setting that allows each source domain to offer a zoo of multiple source models with different architectures. While it enriches the source knowledge, Zoo-MSFDA risks being dominated by suboptimal/harmful models. To address this issue, we theoretically analyze the model selection problem in Zoo-MSFDA, and introduce two principles: transferability principle and diversity principle. Recognizing the challenge of measuring transferability, we subsequently propose a novel Source-Free Unsupervised Transferability Estimation (SUTE). It enables assessing and comparing transferability across multiple source models with different architectures under domain shift, without requiring target labels and source data. Based on above, we introduce a Selection, Ensemble, and Adaptation (SEA) framework to address Zoo-MSFDA, which consists of: 1) source models selection based on the proposed principles and SUTE; 2) ensemble construction based on SUTE-estimated transferability; 3) target-domain adaptation of the ensemble model. Evaluations demonstrate that our SEA framework, with the introduced Zoo-MSFDA setting, significantly improves adaptation performance in 2D image classification tasks. Additionally, our SUTE achieves state-of-the-art performance in transferability estimation.

Traditional medical image segmentation methods are mostly uni-modal approaches solely based on the image modality. Recently, the emergence of text-guided image segmentation methods, by utilizing text annotations to compensate for the quality deficiency in image data, has shown promise for improving medical image segmentation. Despite their success, these methods often experience inadequate utilization of beneficial text information, and have applicability issues in the missing text modality scenario. To address these limitations, in this paper, we propose a Medical Language Mixture of Experts (MLMoE), which introduces multiple sub-experts for extracting more diverse information from medical text. These different experts are then combined by a gating module, thus aggregating beneficial text information to assist the image segmentation. Furthermore, to guarantee its performance in the text-absent scenario, a virtual prompt based distillation module is proposed, which distills the valuable knowledge of MLMoE learned from available text information to the virtual prompt, as an alternative text input. Experimental results on two multi-modal medical segmentation datasets demonstrate the effectiveness of our opposed method, achieving state-of-the-art performance. Code will be available at: https://github.com/Rango-bit/MLMoE.git.

Multi-Source-Free Unsupervised Domain Adaptation (MSFUDA) requires aggregating knowledge from multiple source models and adapting it to the target domain. Two challenges remain: 1) suboptimal coarse-grained (domain-level) aggregation of multiple source models, and 2) risky semantics propagation based on local structures. In this article, we propose an evidential learning method for MSFUDA, where we formulate two uncertainties, i.e. Evidential Prediction Uncertainty (EPU) and Evidential Adjacency-Consistent Uncertainty (EAU), respectively for addressing the two challenges. The former, EPU, captures the uncertainty of a sample fitted to a source model, which can suggest the preferences of target samples for different source models. Based on this, we develop an EPU-Based Multi-Source Aggregation module to achieve fine-grained, instance-level source knowledge aggregation. The latter, EAU, provides a robust measure of consistency among adjacent samples in the target domain. Utilizing this, we develop an EAU-Guided Local Structure Mining module to ensure the trustworthy propagation of semantics. The two modules are integrated into the Evidential Aggregation and Adaptation Framework (EAAF), and we demonstrated that this framework achieves state-of-the-art performances on three MSFUDA benchmarks.

Source-free unsupervised domain adaptation (SFUDA) aims to learn a target domain model using unlabeled target data and the knowledge of a well-trained source domain model. Most previous SFUDA works focus on inferring semantics of target data based on the source knowledge. Without measuring the transferability of the source knowledge, these methods insufficiently exploit the source knowledge, and fail to identify the reliability of the inferred target semantics. However, existing transferability measurements require either source data or target labels, which are infeasible in SFUDA. To this end, firstly, we propose a novel Uncertainty-induced Transferability Representation (UTR), which leverages uncertainty as the tool to analyse the channel-wise transferability of the source encoder in the absence of the source data and target labels. The domain-level UTR unravels how transferable the encoder channels are to the target domain and the instance-level UTR characterizes the reliability of the inferred target semantics. Secondly, based on the UTR, we propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i) the source knowledge calibration module that guides the target model to learn the transferable source knowledge and discard the non-transferable one, and ii) the target semantics calibration module that calibrates the unreliable semantics. With the help of the calibrated source knowledge and the target semantics, the model adapts to the target domain safely and ultimately better. We verified the effectiveness of our method using experimental results and demonstrated that the proposed method achieves state-of-the-art performances on the three SFUDA benchmarks. Code is available at https://github.com/SPIresearch/UTR.

Part-based network is an effective method to improve performance in person re-identification (re-ID). Most existing methods assume the availability of well-aligned person bounding box images as model input. However, automatic detection in some datasets causes misalignment which negatively affects the performance. In this work, we propose an Attentional Part-based CNN (AP-CNN) model which combines learning partial features and attention selection. First, we partition feature map into several horizontal stripes. Second, we use attention selection in each stripe to align the pedestrian images. Inside, we introduce a free-parameter attention model with skip-layer connection which maximizes the complementary information of different levels without increasing the complexity of network. Results on four datasets validate the competitiveness of AP-CNN over the state-of-the-art achieving Rank-1 accuracy of 94.4% on Market-1501, 87.3% on DukeMTMC-ReID, 73.7% on CUHK03-labeled and 72.6% on CUHK03-detected.