Zhejiang Gongshang University

Alibaba Group Holding Limited

State Grid Huzhou Power Supply Company

Zhejiang Tailun Power Group Co., Ltd.

Xun Wang

Huiyan Wang

Leqing Zhu

Jian Wu

Jing Gao

Zhen Wang

Jing Xu

Yanan Zhang

Yongsheng Xu

Weixun Qin

Xiaobin Shen

Jie Chai

Qi Zheng

Xinlong Wu

Shijun Chen

Yili Fang

Ting Zhou

Jianfeng Dong

Jingwei Peng

Size Zhang

Hong Ding

Xiaoxiao Wu

Mingsen Hu

Xuran Zhao

A utility vehicle for power emergency repair includes a cab, a carriage and a lighting device, including mounting bases, a wire pole, a rotary base and a lamp assembly. A rotary shaft is bridged between the mounting bases, and the rotary base is fixedly connected to the rotary shaft. First and second positioning units are provided on the top of the carriage. The rotary base has first and second positioning surfaces, the first positioning unit includes a first limiting platform, and the second positioning unit includes a rotation limiting plate and positioning plates. The positioning plates is supported on a lower end surface of the rotation limiting plate. The utility vehicle further includes a driving assembly and first and second contacts. A trigger signal is generated and the driving assembly is controlled to release braking on the rotary shaft when the first and second contacts contact each other.

Utility vehicle for power emergency repair

A progressive localization method for text-to-video clip localization. The method comprises: first, respectively extracting features of two modes, namely a video mode and a text mode by using different feature extraction methods; then progressively selecting different step sizes, and learning the correlation between the video and the text in multiple stages; and finally, training a model in an end-to-end manner based on the correlation loss of each stage. Moreover, the fine time granularity stage is fused with information of the coarse time granularity stage by means of a condition feature update module and up-sampling connection, such that different stages are mutually promoted. Different stages can pay attention to clips with different time granularities, and the model can cope with the situation that the length of a target clip is obviously changed based on the interrelation between the stages.

Progressive localization method for text-to-video clip localization

Disclosed is a method for performing fine-grained text sentiment analysis based on the users' harshness. According to the method, product attributes are automatically extracted according to comments in a data set, and the comments are split according to the product attributes, so as to achieve the effect of fine-grained analysis. Then, an inference model, which takes users' harshness into consideration, is used, and the inference model takes the users' harshness as hidden variables and solves same by means of an expectation-maximization (EM) algorithm, so as to obtain evaluation results of several product attributes of each product, thereby further improving the accuracy of sentiment analysis. According to the method, the problems in sentiment analysis of analysis granularity being too large and the users' harshness differing are solved, such that high-quality sentiment analysis can be performed on product comments.

Method for analyzing fine-grained text sentiment based on users' harshness

Outputting an entry point to a target service is disclosed, including: determining that a set of sensed data that is obtained from a first device meets a condition associated with a detectable event, wherein the set of sensed data is determined by one or more sensors associated with the first device; in response to the determination, determining a target service corresponding to the detectable event; determining a selected device among the first device and at least one second device, wherein the at least one second device is related to the first device; and outputting an entry point corresponding to the target service to the selected device, wherein the entry point is usable to access the target service.

Outputting an entry point to a target service

A method for generating spatial-temporal consistency depth map sequences based on convolutional neural networks for 2D-3D conversion of television works includes steps of: 1) collecting a training set, wherein each training sample thereof includes a sequence of continuous RGB images, and a corresponding depth map sequence; 2) processing each image sequence in the training set with spatial-temporal consistency superpixel segmentation, and establishing a spatial similarity matrix and a temporal similarity matrix; 3) establishing the convolution neural network including a single superpixel depth regression network and a spatial-temporal consistency condition random field loss layer; 4) training the convolution neural network; and 5) recovering a depth maps of a RGB image sequence of unknown depth through forward propagation with the trained convolution neural network; which avoids that clue-based depth recovery method is greatly depended on scenario assumptions, and inter-frame discontinuity between depth maps generated by conventional neural networks.

Method for generating spatial-temporally consistent depth map sequences based on convolution neural networks

A stereoscopic video generation method based on 3D convolution neural network is disclosed, which is able to convert existing 2D video sources into stereoscopic videos. The method includes preparing the training data, dividing the training video sources into left eye view sequences and right eye view sequences; and then processing the left eye image sequences through shot segmentation via fuzzy C-means clustering method, calculating a mean image of all left eye images, subtracting the mean image from the left eye images, taking the right eye images as a training target; training the obtained 3D convolution neural network through the training data; processing the 2D video sources which need to be converted into stereoscopic videos in the same way as training set, inputting to the trained 3D convolution neural network to obtain the right eye view image sequences of the 2D videos; and finally combining the two to be stereoscopic videos.

Stereoscopic video generation method based on 3D convolution neural network

A depth estimation method for a monocular image based on a multi-scale CNN and a continuous CRF is disclosed in this invention. A CRF module is adopted to calculate a unary potential energy according to the output depth map of a DCNN, and the pairwise sparse potential energy according to input RGB images. MAP (maximum a posteriori estimation) algorithm is used to infer the optimized depth map at last. The present invention integrates optimization theories of the multi-scale CNN with that of the continuous CRF. High accuracy and a clear contour are both achieved in the estimated depth map; the depth estimated by the present invention has a high resolution and detailed contour information can be kept for all objects in the scene, which provides better visual effects.

Depth estimation method for monocular image based on multi-scale CNN and continuous CRF

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Zhejiang, China

Xun Wang