Abstract: A semantic identifier that represents information within a document may be determined by training a language model or other machine learning model using a self-supervised process. The model encodes text to generate an embedding. A decoder determines a semantic identifier token that represents information in the text based on the embedding and a previous semantic identifier token if present. The identifier determined by the decoder is aligned with one of a selected set of semantic identifiers indicated in codebook data. To determine the accuracy of the semantic identifier token, a second decoder is used to attempt to reconstruct the original text using the semantic identifier token and a portion of the original text. Differences between the reconstructed text and the original text are used to determine a loss value, and the parameters of the machine learning model or the codebook data are trained based on minimizing the loss value.

Abstract: An acceleration and compression method for a deep convolutional neural network based on quantization of a parameter provided by the present application comprises: quantizing the parameter of the deep convolutional neural network to obtain a plurality of subcode books and respective corresponding index values of the plurality of subcode books; acquiring an output feature map of the deep convolutional neural network according to the plurality of subcode books and respective corresponding index values of the plurality of subcode books. The present application may implement the acceleration and compression for a deep convolutional neural network.

Abstract: An acceleration and compression method for a deep convolutional neural network based on quantization of a parameter provided by the present application comprises: quantizing the parameter of the deep convolutional neural network to obtain a plurality of subcode books and respective corresponding index values of the plurality of subcode books; acquiring an output feature map of the deep convolutional neural network according to the plurality of subcode books and respective corresponding index values of the plurality of subcode books. The present application may implement the acceleration and compression for a deep convolutional neural network.

Abstract: An image matching method based on cascaded binary encoding includes using a hashing look-up with multiple hashing tables to coarsely filter candidate key-points in an image to produce a candidate subset of key-points, projecting the candidate subset into a high-dimensional Hamming space, and building a “Hamming distance-memory address” hashing table. An optimal matching key-point is discovered by querying the hashing table. The image matching method has high processing speed and matching quality, which can be used for efficient and accurate image matching.

Abstract: This invention involves an image matching method based on the cascaded binary encoding. The stated method includes: Procedure S1, using the hashing look-up with multiple hashing tables to coarsely filter candidate key-points in the image to produce a candidate subset of key-points; Procedure S2, projecting the candidate subset into a high-dimensional Hamming space; Procedure S3, a “Hamming distance-memory address” hashing table is built, and the optimal matching key-point is discovered by querying this hashing table. The image matching method proposed in this invention has high processing speed and matching quality, which can be used for efficient and accurate image matching.

Abstract: A method and system for invoking an operation of a communication terminal in response to registering and interpreting a predetermined motion or pattern of an object. An input is received, the image data of the object is captured and the object in the image data is identified.

Abstract: A method and system for invoking an operation of a communication terminal in response to registering and interpreting a predetermined motion or pattern of an object. An input is received, the image data of the object is captured and the object in the image data is identified.