Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Wenhao Wu

Wei Li

Xinyan Xiao

Jiachen Liu

Yuxiang Zhao

The present disclosure provides a summary generation model training method and apparatus, a device and a storage medium, and relates to the field of computer technologies, and in particular, to the field of artificial intelligence such as natural language processing and deep learning. The summary generation model training method includes: acquiring a document representation corresponding to a document sample; constructing, based on the document representation, a summary representation corresponding to the document representation, the summary representation including a positive summary representation and a negative summary representation; and constructing a total contrastive loss function based on the document representation, the positive summary representation and the negative summary representation, and training a summary generation model based on the total contrastive loss function. The present disclosure may improve accuracy of the summary generation model.

Summary generation model training method and apparatus, device and storage medium

Provided are a video recognition method and apparatus, an electronic device, a medium and a computer program product. The video recognition method is described below. A to-be-recognized video is divided into at least two video segments; video frames are extracted from the at least two video segments, and feature recognition is performed on the video frames to obtain initial semantic feature blocks of the at least two video segments; each of the initial semantic feature blocks is fused, and a fused target semantic feature block is obtained; and a type of the to-be-recognized video is determined according to the fused target semantic feature block.

Video recognition method and apparatus, electronic device and storage medium

A method, an apparatus, an electronic device and a computer readable storage medium for classifying a video are provided. The method may include: acquiring a to-be-classified video and an image complexity of the to-be-classified video; extracting a feature map with a resolution corresponding to the image complexity from the to-be-classified video; and determining a target video class to which the to-be-classified video belongs based on the feature map.

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Beijing, China

Wenhao Wu