According to the World Federation of the Deaf, more than two hundred sign languages exist. Therefore, it is challenging to understand deaf individuals, even proficient sign language users, resulting in a barrier between the deaf community and the rest of society. To bridge this language barrier, we propose a novel multilingual communication system, namely MUGCAT, to improve the communication efficiency of sign language users. By converting recognized specific hand gestures into expressive pictures, which is universal usage and language independence, our MUGCAT system significantly helps deaf people convey their thoughts. To overcome the limitation of sign language usage, which is mostly impossible to translate into complete sentences for ordinary people, we propose to reconstruct meaningful sentences from the incomplete translation of sign language. We also measure the semantic similarity of generated sentences with fragmented recognized hand gestures to keep the original meaning. Experimental results show that the proposed system can work in a real-time manner and synthesize exquisite stunning illustrations and meaningful sentences from a few hand gestures of sign language. This proves that our MUGCAT has promising potential in assisting deaf communication.

基于草图的3D形状检索（SBSR）是一项重要但艰巨的任务，近年来引起了越来越多的关注。现有方法在限制设置中解决了该问题，而无需适当模拟真实的应用程序方案。为了模仿现实的设置，在此曲目中，我们采用了不同级别的绘图技能的业余爱好者以及各种3D形状的大规模草图，不仅包括CAD型号，而且还可以从真实对象扫描的模型。我们定义了两个SBSR任务，并构建了两个基准，包括46,000多个CAD型号，1,700个现实型号和145,000个草图。四个团队参加了这一轨道，并为这两个任务提交了15次跑步，由7个常用指标评估。我们希望，基准，比较结果和开源评估法会在3D对象检索社区中促进未来的研究。

在视频中利用时空冗余的自适应抽样对于在有限的计算机和电池资源的可穿戴设备上始终进行动作识别至关重要。常用的固定采样策略不是上下文感知的，并且可能会在视觉内容下进行样本，从而对计算效率和准确性产生不利影响。受到人类视觉感知机制的动脉视觉和动力前处理的概念的启发，我们引入了一种新型的自适应时空抽样方案，以进行有效的动作识别。我们的系统以低分辨率为扫描前扫视全球场景上下文，并决定跳过或要求在显着区域的高分辨率功能进行进一步处理。我们在Epic-Kitchens和UCF-101数据集上验证该系统以进行动作识别，并表明我们所提出的方法可以大大加快与最先进基线相比的准确性丧失的推断。