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https://www.selleckchem.com/products/asp5878.html CSV-DML is constructed to work directly on the kernel-transformed instances. Specifically, we learn a specific Mahalanobis distance metric from the kernel-transformed training instances and train a DML-based separating hyperplane based on it. An iterated approach is formulated to optimize CSV-DML, which is based on generalized block coordinate descent and can converge to the global optimum. In CSV-DML, since the dimension of kernel-transformed instances is only related to the number of original training instances, we develop a novel parameter reduction scheme for reducing the feature dimension. Extensive experiments show that the proposed CSV-DML method outperforms the previous methods.Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used. In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods. However, the presence of duplicate information and abundant spatiotemporal information in video data poses a serious challenge to video object detection. Therefore, in recent years, many scholars have investigated deep learning detection algorithms in the context of video data and have achieved remarkable results. Considering the wide range of applications, a comprehensive review of the research related to video object detection is both a necessary and challenging task. This survey attempts to link and systematize the latest cutting-edge research on video object detection with the goal of classifying and analyzing video detection algorithms based on specific representative models. The differences and connections between video object detection and similar tasks are systematically demonstrated, and the evaluation metrics and video detection performance of nearly 40 models on two data sets are presented. Finally, the various applications and challenges facin

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