Jinan University

Macalester College

Tingrong Zhi

Feiran Huang

Shuyuan Lin

Zihong Yang

Xiaocheng Lin

Yijie Zhang

The present invention relates to the field of artificial intelligence, and in particular, to a camera pose estimation method and system, electronic equipment and a readable medium. The camera pose estimation method includes: acquiring an initial matching set between a first image and a second image, where the first image and the second image are images from different angles for a same scene; performing a mismatch removal operation on the initial matching set based on an optimization network to obtain an optimized matching set, where the optimization network is constructed based on a multi-stage geometric semantic attention network; and acquiring a camera pose result based on the optimized matching set. By removing mismatches, feature matching results between the first image and the second image are more accurate, and thus a more accurate result can be obtained when performing camera pose estimation.

Camera pose estimation method and system, electronic equipment and readable medium

The embodiment of the invention discloses a diversified recommendation method for news based on graph neural network and its device, which comprises the followings: word segmentation processing is performed on the target news text to obtain a word segmentation set, and vectorization processing is performed on the word segmentation set, so that each word segmentation in the word segmentation set has a word segmentation embedding vector; vectorization processing is performed on a target user group, so that each user in the target user group has a user embedding vector; the word segmentation embedding vector and the user embedding vector are input into the attention mechanism algorithm to obtain the news embedding vector; a graph neural network is constructed according to the user embedding vector and the news embedding vector, and the recommendation model is trained to obtain diversified recommendation results. In the embodiment, firstly, a word segmentation tool is used to segment each news text, and then the embedding vector of news is obtained by using attention mechanism, user interest and word segmentation information, and the corresponding expression is obtained by graph neural network, and the model is optimized, and finally the diversified recommendation results are obtained by calculation.

Diversified recommendation method for news based on graph neural network and its device

A specific target-oriented social media tweet sentiment analysis method includes: preprocessing social media tweet data to obtain a target text and a specific target; passing the target text and the specific target through an embedding layer to obtain target text word vectors and obtain a specific target word vector, respectively; passing the target text word vectors through a self-attention structure to obtain a self-attention result; combining the self-attention result with the specific target word vector and passing through a cross-attention structure to obtain cross-attention results; concatenating the cross-attention results to obtain an attention representation matrix; and passing the attention representation matrix through a pooling layer, a fully connected layer and a softmax layer to obtain a sentiment tendency of the specific target. The method, based on an attention mechanism in the transformer structure, fuses a local attention mechanism and an attention mechanism containing position information, the analysis accuracy is improved.

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Guangzhou, China

Tingrong Zhi