Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

综放工作面防治水技术应用实践

为解决21000工作面采空区及顶板涌水问题,根据工作面出水点的不同情况,采取了集水器导水、老塘设挡水堰截水、挖排水沟疏水等多项治水措施,经统计排水量达33~42m3/h,有效控制了水情,确保了工作面正常生产。     

Optimal Guaranteed Cost Tracking of Uncertain Nonlinear Systems Using Adaptive Dynamic Programming with Concurrent Learning

In this paper, based on adaptive dynamic programming (ADP) with concurrent learning, the problem of optimal guaranteed cost tracking is studied for a class     

An airlight estimation method for image dehazing based on gray projection

Multimedia Tools and Applications - As the key parameter of dehazing algorithms, airlight value directly affect the calculation accuracy of sky region, and any deviation will lead to the chromatic...     

Within-class multimodal classification

Multimedia Tools and Applications - In many real-world classification problems there exist multiple subclasses (or clusters) within a class; in other words, the underlying data distribution is...