This article explores whether there are significant differences in the creativity level of future teachers in teaching design. In virtual reality (VR) and mixed reality (MR) experimental teaching environments, the K-DOCS creative power scale, the flow state scale, and the expert group assessment scale were used to investigate the mediation effect of flow, attention, and meditation on the creativity level. The results indicate that in the MR experimental teaching environment, the three factors have a moderate mediating effect on the creativity level of future teachers (n = 65), and meditation has a certain regulating effect on heart flow (0.175*); In the VR experimental teaching environment (n = 67), only the factor of heart flow has a moderate promotion effect on the creativity level of future teachers (0.822***). The study results indicate that the quality of future teachers’ creative instructional design in the MR environment is higher than that in the VR environment. In addition more MR experimental teaching environments should be provided to cultivate the creativity level of future teachers in teaching design.

In this paper, we study the task of referring semantic segmentation in a highly practical setting, in which labeled visual data with corresponding text descriptions are available in the source, but only unlabeled visual data (without text descriptions) are available in the target. It is a challenging task that has many difficulties: (1) how to obtain proper queries for the target domain; (2) how to adapt visual-text joint distribution shifts; (3) how to maintain the original segmentation performance. Thus, we propose a cycle-consistent vision-language matching network to narrow down the domain gap and ease adaptation difficulty. Our model has significant practical applications since they are capable generalising to new data sources without requiring corresponding text annotations. First, a pseudo-text selector is devised to handle the missing modality, through the pre-trained clip model to measure the gap between query features of the source and visual features of the target. Next, a cross-domain segmentation predictor is adopted, which prompts the joint representations to be domain invariant and minimize the discrepancy between two domains. Then, we present a cycle-consistent query matcher to learn discriminative features via reconstructing visual features from masks. Instead of doing the textual comparison, we match the visual features to the pseudo queries. Extensive experiments show the effectiveness of our method.

Sign Language Production (SLP) aims to translate a spoken language description to its corresponding continuous sign language sequence. A prevailing solution for this problem is in a two-staged manner: it formulates SLP as two sub-tasks, i.e., Text to Gloss (T2G) translation and Gloss to Pose (G2P) animation, with gloss annotations as pivots. Although two-staged approaches achieve better performance than their direct translation counterparts, the requirement of gloss intermediaries causes a parallel data bottleneck. In this paper, to reduce reliance on gloss annotations in two-staged approaches, we propose DualSign, a semi-supervised two-staged SLP framework, which can effectively utilize partially gloss-annotated text-pose pairs and monolingual gloss data. The key component of DualSign is a novel Balanced Multi-Modal Multi-Task Dual Transformation (BM3T-DT) method, where two well-designed models, i.e., a Multi-Modal T2G model (MM-T2G) and a Multi-Task G2P model (MT-G2P), are jointly trained by leveraging their task duality and unlabeled data. After applying BM3T-DT, we derive the expected uni-modal T2G model from the well-trained MM-T2G with knowledge distillation. Considering that the MM-T2G may suffer from modality imbalance when decoding with multiple input modalities, we devise a cross-modal balancing loss, further boosting the system's overall performance. Extensive experiments conducted on the PHOENIX14T dataset show the effectiveness of our approach in the semi-supervised setting. By training with additionally collected unlabeled data, DualSign substantially improves previous state-of-the-art SLP methods.

Abstract We present a simulation microscope device called GiantScope, which combines virtual microscope, cloud computing, and embedded technologies. Users can complete most of the microscope‐based experiments in biology courses by operating our device, while learning the operating skills of microscopes at the same time. Our device supports most of the operation functions of optical microscopes, including quasifocus screw adjustment, slide movement recognition and so on, and also has auxiliary enhancement functions including manual measurement, annotation and so on. In addition, we have built a cloud‐based digital slide database, which enables users to select experimental observations through digital slides, including static cell specimens or dynamic cell activities. After user study, we found that using GiantScope for biological experiments has better learning efficiency and user experience than traditional microscopes.

This paper studies a set of MR technologies for middle school experimental teaching environments and develops a multi-channel MR user interface called Dream-Experiment. The goal of Dream-Experiment is to improve the traditional MR user interface, so that users can get a real, natural 3D interactive experience like real experiments, but without danger and pollution. In terms of visual presentation, we design multi-camera collaborative registration to realize robust 6-DoF MR interactive space, and also define a complete rendering pipeline to provide improved processing of virtual-real objects' occlusion including translucent devices. In the virtual-real interaction, we provide six interaction modes that support visual interaction, tangible interaction, virtual-real gestures with touching, voice, thermal feeling, and olfactory feeling. After users' testing, we find that Dream-Experiment has better interactive efficiency and user experience than traditional MR environments.