Person re-identification (Re-ID) has achieved great success in single-domain. However, it remains a challenging task to adapt a Re-ID model trained on one dataset to another one. Unsupervised domain adaption (UDA) was proposed to migrate a model from a labeled source domain to an unlabeled target domain. The main difference in the cross-domain is different background styles. Although the style transfer approach effectively reduces inter-domain gaps, it ignores the reduction of intra-class differences. Clustering-based pipelines maintain state-of-the-art performance for UDA by learning domain-independent features; however, most existing models do not sufficiently exploit the rich unlabeled samples in target domains due to unsatisfactory clustering. Thus, we propose a novel local correlation ensemble model that focuses on the diversity of intra-class information and the reliability of class centers. Specifically, a pedestrian attention module is proposed to enable the encoder to pay more attention to the person’s features to relieve interference caused by the shared background style. Furthermore, we propose a priority-distance graph convolutional network (PDGCN) module that employs a graph convolutional network network to predict the priority of a node as a class center and then calculates the distance between nodes with high priority values to screen out the class center nodes. Finally, the encoder features (local) and PDGCN features (context-aware) are combined to perform person Re-ID. The results of experiments on the large-scale public Re-ID datasets verified the effectiveness of the proposed method.

Downsampling is a crucial task for processing large scale and/or dense point clouds with limited resources. Owing to the development of deep learning, approaches of task-oriented point cloud downsampling have significant performance gains in preserving geometric information. However, most downsamling methods are limited by the disordered and unstructured point cloud data, making it difficult to continually improve the performance. To address this issue, we propose a light-weight Transformer network (LighTN) for the task-oriented point cloud downsampling as an end-to-end solution. In LighTN, we design an energy-efficient and permutation invariant single-head self-correlation module to extract refined global geometric features. Moreover, we present a novel sampling loss function to guide LighTN to focus on critical point cloud regions with more uniform distributions and prominent point coverage. Extensive experiments on classification, registration, and reconstruction tasks demonstrate that LighTN can achieve the state-of-the-art performance-overhead tradeoff and high-quality qualitative results.

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other. In this work, we recognize that there is a significant semantic gap between features at the intermediate feature layer and class labels at the final output layer. To bridge this gap, we develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity in a contrastive learning setting. This contrastive Bayesian analysis leads to a new loss function for deep metric learning. To improve the generalization capability of the proposed method onto new classes, we further extend the contrastive Bayesian loss with a metric variance constraint. Our experimental results and ablation studies demonstrate that the proposed contrastive Bayesian metric learning method significantly improves the performance of deep metric learning in both supervised and pseudo-supervised scenarios, outperforming existing methods by a large margin.

Autism spectrum disorder (ASD) is a developmental disability that can impair communication, social skills, living skills, and learning capabilities. Learning approaches usually differ between mainstream schools and special needs schools, to cater for the different learning processes of children with ASD. Besides the traditional classroom-based education, alternative technology and methods are explored for special needs education. One method is to train children with ASD using Virtual Reality (VR) technologies. Many prior works show the effectiveness of VR-based learning with varying degrees of success. Some children with ASD may face challenges to gain independent living skills. Their parents or guardians have to expend a significant amount of effort in taking care of children with ASD. It will be very helpful if they can have a learning opportunity to gain such living skills. In this research, we develop a VR serious game to train children with ASD one of the basic living skills for road crossing safely. The VR serious game operates on multiple types of platforms, with various user interaction inputs including the Microsoft Kinect sensor, keyboard, mouse, and touch screen. The game design and methodology will be described in this paper. Experiments have been conducted to evaluate the learning effectiveness of the road crossing game, with very positive results achieved in the quiz and survey questionnaire after the gameplay.

The block-based representation learning method has been proven to be a very effective method for person re-identification (Re-ID), but the features extracted by the existing block-based approach tend to have a high correlation among different blocks. Also, these methods perform less well for persons with large posture changes. Thus, Part-based Nondirect Coupling (PNC) representation learning method is proposed by introducing a similarity measure loss to constrain features of different blocks. Moreover, Part-based Nondirect Coupling Embedded GAN (PGAN) method is proposed, which aims to extract more common features of different postures of a same person. In this way, the extracted features of the network are robust for posture changes of a person, and there are no auxiliary pose information and additional computational cost required in the test stage. Experimental results on public datasets show that our proposed method achieves good performances, especially, it outperforms the state-of-the-art GAN-based methods for person Re-ID.