Deformable image registration aims to achieve nonlinear alignment of image spaces by estimating dense displacement fields. It is widely used in clinical tasks such as surgical planning, assisted diagnosis, and surgical navigation. While efficient, deep learning registration methods often struggle with large, complex displacements. Pyramid-based approaches address this with a coarse-to-fine strategy, but their single-feature processing can lead to error accumulation. In this paper, we introduce a dense Mixture of Experts (MoE) pyramid registration model, using routing schemes and multiple heterogeneous experts to increase the width and flexibility of feature processing within a single layer. The collaboration among heterogeneous experts enables the model to retain more precise details and maintain greater feature freedom when dealing with complex displacements. We use only deformation fields as the information transmission paradigm between different levels, with deformation field interactions between layers, which encourages the model to focus on the feature location matching process and perform registration in the correct direction. We do not utilize any complex mechanisms such as attention or ViT, keeping the model at its simplest form. The powerful deformable capability allows the model to perform volume registration directly and accurately without the need for affine registration. Experimental results show that the model achieves outstanding performance across four public datasets, including brain registration, lung registration, and abdominal multi-modal registration. The code will be published at https://github.com/Darlinglinlinlin/MOE_Morph.

High-quality pseudo-labels are critical for guiding learning in semi-supervised change detection (SSCD). Recently, many SSCD methods based on consistency regularization (CR) have achieved advanced performance. These methods typically generate pseudo-labels by setting a fixed threshold. However, this strategy struggles to generate pseudo-labels with high-quality boundary details. To this end, we propose a novel SSCD method boundary refinement teacher (BRT) to enhance the boundary quality of pseudo-labels. A bi-temporal image boundary refinement (BIBR) module is designed to uncover boundary details in unlabeled images at first. BIBR explores the boundary characteristics of pseudo-label by extracting the boundary blocks from its change map and re-delineates the boundary decision points with their magnified views. Then, a stable teacher parameter update (STPU) module is devised to sustain the semi-supervised learning state steady, avoiding frequent updates to the teacher model parameters. These stable updates to teacher model parameters provide continuous and high-quality guidance, preventing pseudo-label fluctuations from disrupting the student model’s acquisition of new knowledge. Extensive experiments are conducted on three commonly used change detection datasets, encompassing buildings and multiple categories, covering SSCD settings, boundary metrics, and detailed ablation studies. Our results show that simply enhancing the boundary quality of the pseudo-labels allows BRT to consistently deliver state-of-the-art (SOTA) performance in SSCD. The code is available at: https://github.com/yoghurts-sy/BRT.

The traditional view-aware based vehicle re-identification methods integrated the vehicle view information into the training process, allowing the model parameters to learn the vehicle view knowledge. So, the extracted vehicle features contained vehicle view information, and the impact of view differences on model performance can be reduced. Since ensemble training with different methods will affect each other, it is difficult for these methods to train models together with other non view-aware methods. In order to address this challenge, in this paper, we propose a view-aware-based post-processing method (VABPP), which uses vehicle view information to re-rank the re-identification results during testing. According to vehicle views, VABPP method divide the distances between vehicle id features into several groups. During testing, multiply the distances of each different group by different coefficients. So, it treats different views equally. In order to achieve better implementation of this post-processing method, in this paper, we also propose a scheme that unifies the feature distance distributions of the training set and the test set. This scheme can enable some properties of the training set to be directly used in the test set. As the properties of the training set are trained under the guidance of correctly labeled labels, which enhances the robustness of the test set properties. The mAP in VeRi-776 dataset and in the three test sets of VERI-Wild dataset are 83.7%, 88.7%, 84.6% and 78.5%, respectively. And the rank-1 in the three test sets of VehicleID dataset are 88.6%, 85.4% and 81.1%, respectively.

Video Anomaly Detection (VAD) presents a substantial challenge in the field of computer vision. The emergence of self-supervised learning has played a crucial role in tackling this challenge, with the design of self-supervised pretext tasks proving to be an exceptionally effective method. However, how to imbue the devised pretext task with a more comprehensive understanding of video content is still a problem worth exploring. In this paper, we contribute by introducing two object-level self-supervised tasks tailored for mining temporal and spatial information in videos, subsequently applied to VAD. The self-supervised pretext tasks we have formulated are as follows: (i) Frame Rotation Prediction and (ii) Frame Interval Prediction. These two pretext tasks Focus on handling abnormality of appearance and action respectively. Our approach follows an end-to-end methodology that does not depend on pre-trained models, skeleton data, or optical flow information. In our experimental evaluations, our model has demonstrated superior performance, outperforming established competitors on two publicly available benchmarks. In particular, we achieved a micro-AUROC of 86.9 on the ShanghaiTech dataset.

Online literature circles can be powerful and beneficial for promoting the learning of English as a foreign language (EFL). This study aims to investigate the influence of using online literature circles as an instructional method on EFL students’ English self-efficacy. This study involves 228 second-year college students at a university in North China. A pretest-posttest quasi-experimental design was conducted to examine whether students’ English self-efficacy would change after their participation in the online literature circles activities. The results indicated that the students’ self-efficacy in English listening, speaking, reading, and writing has all improved after participating in online literature circles activities.