Disclosed herein is Arrowland, a web-based software tool for inputting, managing and viewing multiomics data, such as transcriptomics, proteomics, metabolomics and fluxomics data in an interactive, intuitive and multiscale system.

The present technology analyzes the content of images to create complex representations of the images and then reduces the complexity of these representations into a size that is both suitable for comparison but also contains critical image descriptive aspects. These reduced complexity representations can then be used to efficiently search for similar images. Moreover, the reduced complexity representations are formatted such that they can take advantage of existing text search engines, which are well suited to efficiently searching through a large number of unique results.

The present technology analyzes the content of images to create complex representations of the images and then reduces the complexity of these representations into a size that is both suitable for comparison but also contains critical image descriptive aspects. These reduced complexity representations can then be used to efficiently search for similar images. Moreover, the reduced complexity representations are formatted such that they can take advantage of existing text search engines, which are well suited to efficiently searching through a large number of unique results.

Embodiments of the specification provide an image search method, an apparatus, and a device. The method includes: obtaining an input image associated with an image search, wherein the input image includes a plurality of first text blocks; selecting a to-be-processed image from a target database, wherein the to-be-processed image includes a plurality of second text blocks; and generating a first graph structural feature based on the plurality of first text blocks; generating a second graph structural feature based on the plurality of second text blocks; determining that the first graph structural feature and the second graph structural feature satisfy a condition; and in response to determining that the first graph structural feature and the second graph structural feature satisfy the condition, outputting the to-be-processed image as a search result.

Embodiments of the specification provide an image search method, an apparatus, and a device. The method includes: obtaining an input image associated with an image search, wherein the input image includes a plurality of first text blocks; selecting a to-be-processed image from a target database, wherein the to-be-processed image includes a plurality of second text blocks; and generating a first graph structural feature based on the plurality of first text blocks; generating a second graph structural feature based on the plurality of second text blocks; determining that the first graph structural feature and the second graph structural feature satisfy a condition; and in response to determining that the first graph structural feature and the second graph structural feature satisfy the condition, outputting the to-be-processed image as a search result.

An image search method and apparatus, an electronic device and a storage medium. The image search method can be applied to an electronic device, which comprises a camera. The electronic device acquires an image to be matched, a first position of the electronic device and the azimuth of the camera, determines from a preset scene database a target preset area in which the first position is located and a target preset azimuth interval in which the azimuth is located, and searches for a corresponding target preset image set, thereby determining a target image for the image to be matched.

An image search method and apparatus, an electronic device and a storage medium. The image search method can be applied to an electronic device, which comprises a camera. The electronic device acquires an image to be matched, a first position of the electronic device and the azimuth of the camera, determines from a preset scene database a target preset area in which the first position is located and a target preset azimuth interval in which the azimuth is located, and searches for a corresponding target preset image set, thereby determining a target image for the image to be matched.

Disclosed are a vector diagram generation method and apparatus, and a storage medium, the method being used in an FPGA. The method comprises: acquiring video data of an ultra-high definition video system; on the basis of the video data, generating vector diagram data; acquiring a pre-generated background image of the vector diagram; and, on the basis of the background image and the vector diagram data, generating a vector diagram. The vector diagram generation method and apparatus and storage medium provided in the present disclosure can better implement vector diagram generation of the ultra-high definition video system. (FIG. 1)

A training method for a robust neural network based on feature matching is provided in this disclosure, which includes following steps. Step A, a first stage model is initialized. The first stage model includes a backbone network, a feature matching module and a fullple loss function. Step B, the first stage model is trained by using original training data to obtain a second stage model. Step C, the second stage model is attacked so as to generate PGD adversarial samples of the original training data, and the second stage model is trained again with the generated adversarial samples and the original training data. Step D, training parameters are adjusted and the second stage model is trained again, and parameters for which the model has highest accuracy on an original test set are saved.

A training method for a robust neural network based on feature matching is provided in this disclosure, which includes following steps. Step A, a first stage model is initialized. The first stage model includes a backbone network, a feature matching module and a fullple loss function. Step B, the first stage model is trained by using original training data to obtain a second stage model. Step C, the second stage model is attacked so as to generate PGD adversarial samples of the original training data, and the second stage model is trained again with the generated adversarial samples and the original training data. Step D, training parameters are adjusted and the second stage model is trained again, and parameters for which the model has highest accuracy on an original test set are saved.