According to an embodiment, a pattern recognition device is configured to divide an input signal into a plurality of elements, convert the divided elements into feature vectors having the same dimensionality to generate a set of feature vectors, and evaluate the set of feature vectors using a recognition dictionary including models corresponding to respective classes, to output a recognition result representing a class or a set of classes to which the input signal belongs. The models each include sub-models each corresponding to one of possible division patterns in which a signal to be classified into a class corresponding to the model can be divided into a plurality of elements. A label expressing a model including a sub-model conforming to the set of feature vectors, or a set of labels expressing a set of models including sub-models conforming to the set of feature vectors is output as the recognition result.

A method of an escape reorder mode for neural network model compression, is performed by at least one processor, and includes determining whether a frequency count of a codebook index included in a predicted codebook is less than a predetermined value, the codebook index corresponding to a neural network. The method further includes, based on the frequency count of the codebook index being determined to be greater than the predetermined value, maintaining the codebook index, and based on the frequency count of the codebook index being determined to be less than the predetermined value, assigning the codebook index to be an escape index of 0 or a predetermined number. The method further includes encoding the codebook index, and transmitting the encoded codebook index.

A method of an escape reorder mode for neural network model compression, is performed by at least one processor, and includes determining whether a frequency count of a codebook index included in a predicted codebook is less than a predetermined value, the codebook index corresponding to a neural network. The method further includes, based on the frequency count of the codebook index being determined to be greater than the predetermined value, maintaining the codebook index, and based on the frequency count of the codebook index being determined to be less than the predetermined value, assigning the codebook index to be an escape index of 0 or a predetermined number. The method further includes encoding the codebook index, and transmitting the encoded codebook index.

Disclosed is an image description method and apparatus, a computing device and a storage medium, an example method includes: performing feature extraction on a target image with a plurality of first feature extraction models to obtain image features generated by each of the first feature extraction models; performing fusion processing on the image features generated by the plurality of first feature extraction models to generate global image features corresponding to the target image; performing feature extraction on the target image with a second feature extraction model to obtain target detection features corresponding to the target image; inputting the global image features corresponding to the target image and the target detection features corresponding to the target image into a translation model to generate a translation sentence, and taking the translation sentence as a description sentence of the target image.

Disclosed is an image description method and apparatus, a computing device and a storage medium, an example method includes: performing feature extraction on a target image with a plurality of first feature extraction models to obtain image features generated by each of the first feature extraction models; performing fusion processing on the image features generated by the plurality of first feature extraction models to generate global image features corresponding to the target image; performing feature extraction on the target image with a second feature extraction model to obtain target detection features corresponding to the target image; inputting the global image features corresponding to the target image and the target detection features corresponding to the target image into a translation model to generate a translation sentence, and taking the translation sentence as a description sentence of the target image.

Embodiments relate to a method for determining a search region including acquiring object information of a target object included in an image query, generating a set of non-image features of the target object based on the object information, setting a search candidate region based on a user input, acquiring information associated with the search candidate region from a region database, and determining a search region based on at least one of the information associated with the search candidate region or at least part of the set of non-image features, and a system for performing the same.

Embodiments relate to a method for determining a search region including acquiring object information of a target object included in an image query, generating a set of non-image features of the target object based on the object information, setting a search candidate region based on a user input, acquiring information associated with the search candidate region from a region database, and determining a search region based on at least one of the information associated with the search candidate region or at least part of the set of non-image features, and a system for performing the same.

The present disclosure relates to systems and methods for image compensation. The systems may obtain a reconstructed image frame. The systems may determine a primary classification manner of pixels in the reconstructed image frame. The systems may determine a primary compensation value of each category of pixels in the reconstructed image frame that are classified based on the primary classification manner. The systems may obtain a compensated image frame by compensating the reconstructed image frame based on the primary compensation value of each category of pixels in the reconstructed image frame.

The present disclosure relates to systems and methods for image compensation. The systems may obtain a reconstructed image frame. The systems may determine a primary classification manner of pixels in the reconstructed image frame. The systems may determine a primary compensation value of each category of pixels in the reconstructed image frame that are classified based on the primary classification manner. The systems may obtain a compensated image frame by compensating the reconstructed image frame based on the primary compensation value of each category of pixels in the reconstructed image frame.

Embodiments relate to a method for determining a search region including acquiring object information of a target object included in an image query, generating a set of non-image features of the target object based on the object information, setting a search candidate region based on a user input, acquiring information associated with the search candidate region from a region database, and determining a search region based on at least one of the information associated with the search candidate region or at least part of the set of non-image features, and a system for performing the same.