33results about How to "Rich semantic features" patented technology

The invention relates to image processing technology, in particular to a low-resolution pedestrian detection method, system and storage medium. The method of the present invention includes a training process and a testing process. The training process first determines the training set and the parameters of the training process; then inputs pictures in sequence according to the batch size, extracts the multi-scale features of the training pictures, and reconstructs and enhances the shallow features. Form a new multi-scale detection framework; finally perform frame classification and position regression, calculate training loss and backpropagation, and update weight parameters. The test process first determines the test set, uses the model obtained during the training process as the test model of the algorithm, sequentially inputs test pictures in small batches, extracts multi-scale features, reconstructs and enhances shallow features, and then classifies and positions frames return. The present invention uses a deep learning network to reconstruct shallow features, and at the same time improves the effectiveness of shallow features, so as to enhance the ability to detect low-resolution pedestrians.

The invention discloses a text generation method and device for a construction industry information service question answering system, belonging to the technical fields of artificial intelligence and natural language processing. The technical problem to be solved in the present invention is how to prevent the lack of inter-layer features in the encoding process of the generative question answering system and the feature loss in the decoding process, so as to ensure the accuracy of text generation. The technical solution adopted is: by constructing and training the The text generation model composed of the embedded module, the encoder module, the hidden state mutual information module, the hidden variable mutual information module and the decoder module realizes the multi-layer encoding of the original text and obtains the text representation and hidden information of the original text; The hidden state of the hierarchical encoding, the hidden variables of the original text and their sampling information are used to calculate the mutual information and maximize their mutual information; the original text and the target text are encoded, and the correlation information between the two is obtained through the attention mechanism, and finally the achieve the purpose of text generation.