Abstract: Methods and apparatuses for determining a similarity between text and a video are described. In an example, an initial text feature and an initial video feature that correspond to text and a video are respectively obtained by using a text feature extraction model and a video feature extraction model. The initial text feature is processed based on a syntactic level analysis result of the text, to obtain text features that correspond to elements in the syntactic level analysis result. A video level analysis result corresponding to the syntactic level analysis result is constructed based on a degree of matching between the text features and the initial video feature. Video features corresponding to elements in the video level analysis result are obtained. A similarity between the text and the video is determined based on a similarity between a text feature and a video feature corresponding to elements in a corresponding level.

Abstract: A computer-implemented method includes determination of n noised codes corresponding to n video frames of an original video. A text code corresponding to a description text guiding video editing is determined. Denoising processing, using n Unet models obtained by using the text code and copying a Unet model, is performed on the n noised codes, where a pre-trained text-to-image model includes the Unet model, which includes a self-attention layer connected after a target network layer, and where the denoising processing includes performing, in a self-attention layer of any ith Unet model, attention calculation based on an output of a target network layer of the ith Unet model and an output of a target network layer in a predetermined target Unet model. Decoding processing is separately performed on the n denoised codes by an image decoder to obtain n target images to form an edited target video.

Abstract: A computer-implemented system for encoding includes an encoding layer and at least one joint encoding unit. The encoding layer encodes a received first modal initial feature vector and a received second modal initial feature vector, to generate, respectively, a first modal feature vector and a second modal feature vector, joint encoded by the at least one joint encoding unit, where the at least one joint encoding unit includes an encoding module and a modal input switching module. The modal input switching module processes the first modal feature vector and the second modal feature vector, to obtain, respectively a first modal switching encoding vector and a second modal switching encoding vector. The encoding module processes the first modal switching encoding vector and the second modal switching encoding vector, to generate, respectively a first target modal fusion vector and a second target modal fusion vector.

Abstract: One embodiment facilitates recognizing parts of a vehicle. A convolution module is configured to generate a convolution feature map of a vehicle image. A region proposal module is configured to determine, based on the convolution feature map, one or more proposed regions, wherein a respective proposed region corresponds to a target of a respective vehicle part. A classification module is configured to determine a class and a bounding box of a vehicle part corresponding to a proposed region based on a feature of the proposed region. A conditional random field module is configured to optimize classes and bounding boxes of the vehicle parts based on correlated features of the corresponding proposed regions. A reporting module is configured to generate a result which indicates a list including an insurance claim item and corresponding damages based on the optimized classes and bounding boxes of the vehicle parts.

Abstract: One embodiment facilitates recognizing parts of a vehicle. A convolution module is configured to generate a convolution feature map of a vehicle image. A region proposal module is configured to determine, based on the convolution feature map, one or more proposed regions, wherein a respective proposed region corresponds to a target of a respective vehicle part. A classification module is configured to determine a class and a bounding box of a vehicle part corresponding to a proposed region based on a feature of the proposed region. A conditional random field module is configured to optimize classes and bounding boxes of the vehicle parts based on correlated features of the corresponding proposed regions. A reporting module is configured to generate a result which indicates a list including an insurance claim item and corresponding damages based on the optimized classes and bounding boxes of the vehicle parts.