Movie and TV subtitles are frequently employed in natural language processing (NLP)applications, but there are limited Japanese-Chinese bilingual corpora accessible as a dataset to trainneural machine translation (NMT) models. In our previous study, we effectively constructed a corpusof a considerable size containing bilingual text data in both Japanese and Chinese by collectingsubtitle text data from websites that host movies and television series. The unsatisfactory translationperformance of the initial corpus, Web-Crawled Corpus of Japanese and Chinese (WCC-JC 1.0), waspredominantly caused by the limited number of sentence pairs. To address this shortcoming, wethoroughly analyzed the issues associated with the construction of WCC-JC 1.0 and constructed theWCC-JC 2.0 corpus by first collecting subtitle data from movie and TV series websites. Then, wemanually aligned a large number of high-quality sentence pairs. Our efforts resulted in a new corpusthat includes about 1.4 million sentence pairs, an 87% increase compared with WCC-JC 1.0. As aresult, WCC-JC 2.0 is now among the largest publicly available Japanese-Chinese bilingual corporain the world. To assess the performance of WCC-JC 2.0, we calculated the BLEU scores relative toother comparative corpora and performed manual evaluations of the translation results generated bytranslation models trained on WCC-JC 2.0. We provide WCC-JC 2.0 as a free download for researchpurposes only.

The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied in virtual reality, video surveillance, and video retrieval. With the advancement of deep learning algorithms and computer hardware, the conventional two-dimensional convolution technique for training video models has been replaced by three-dimensional convolution, which enables the extraction of spatio-temporal features. Specifically, the use of 3D convolution in human behavior recognition has been the subject of growing interest. However, the increased dimensionality has led to challenges such as the dramatic increase in the number of parameters, increased time complexity, and a strong dependence on GPUs for effective spatio-temporal feature extraction. The training speed can be considerably slow without the support of powerful GPU hardware. To address these issues, this study proposes an Adaptive Temporal Compression (ATC) module that serves as a standalone module and can be integrated into existing architectures. The ATC module effectively reduces GPU computing load and time complexity with negligible loss of accuracy, thereby facilitating real-time human behavior recognition.

Multi-object tracking (MOT) algorithms are typically classified as one-shot or two-step algorithms. The one-shot MOT algorithm is widely studied and applied due to its fast inference speed. However, one-shot algorithms include two sub-tasks of detection and re-ID, which have conflicting directions for model optimization, thus limiting tracking performance. Additionally, MOT algorithms often suffer from serious ID switching issues, which can negatively affect the tracking effect. To address these challenges, this study proposes the DETrack algorithm, which consists of feature decomposition and feature enhancement modules. The feature decomposition module can effectively exploit the differences and correlations of different tasks to solve the conflict problem. Moreover, it can effectively mitigate the competition between the detection and re-ID tasks, while simultaneously enhancing their cooperation. The feature enhancement module can improve feature quality and alleviate the problem of target ID switching. Experimental results demonstrate that DETrack has achieved improvements in multi-object tracking performance, while reducing the number of ID switching. The designed method of feature decomposition and feature enhancement can significantly enhance target tracking effectiveness.