Abstract: This application provides an image classification method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product. The method includes: obtaining a to-be-classified image including a plurality of to-be-classified sub-images, and converting the to-be-classified image between different modalities, to obtain modality information of the to-be-classified image that corresponds to each modality; performing modality fusion on all pieces of modality information, to obtain a multimodal vector corresponding to the to-be-classified image; encoding the to-be-classified image based on the multimodal vector, to obtain a to-be-classified sub-vector in a one-to-one correspondence with each to-be-classified sub-image; and classifying the to-be-classified image based on the to-be-classified sub-vector corresponding to each to-be-classified sub-image, to obtain the category of the to-be-classified image.

Abstract: An image acquisition model training method is contemplated. The method includes: acquiring a label image pair of a sample image; acquiring a predicted image pair of the sample image according to a first network model; adjusting the first network model based on the label image pair and the predicted image pair to obtain a second network model, so as to reduce a difference, obtained according to the second network model, between the predicted image pair and the label image pair; and determining the second network model as an image acquisition model in response to the second network model satisfying a training termination condition. By the above method, a heat map of an image in Hough space can be determined more quickly, and an image detection result can be determined based on the heat map of the image in the Hough space, thereby improving the accuracy of subsequent analysis processing.

Abstract: This application discloses a spatial positioning method and related apparatus, and a navigation stick.

Abstract: An image acquisition model training method is contemplated. The method includes: acquiring a label image pair of a sample image; acquiring a predicted image pair of the sample image according to a first network model; adjusting the first network model based on the label image pair and the predicted image pair to obtain a second network model, so as to reduce a difference, obtained according to the second network model, between the predicted image pair and the label image pair; and determining the second network model as an image acquisition model in response to the second network model satisfying a training termination condition. By the above method, a heat map of an image in Hough space can be determined more quickly, and an image detection result can be determined based on the heat map of the image in the Hough space, thereby improving the accuracy of subsequent analysis processing.

Abstract: An organic frame material having isopoly-molybdic acid metal for manufacturing polylactic acid and method of manufacturing the same is provided. The chemical formula of the organic frame material having isopoly-molybdic acid metal is [Cu(tfbtb)0.5(?-Mo8O26)0.5(H2O)]. tfbtb is a 2,3,5,6-tetrafluoro-bis (1,2,4-triazole-1-methyl) benzene ligand, and [?-Mo8O26] is a ? type octamolybdate anion. Sodium molybdate dihydrate, copper salt and organic ligand 2,3,5,6-tetrafluoro-bis (1,2,4-triazole-1-methyl) benzene undergo hydrothermal reaction in a closed condition to form the organic frame material having copper containing isopoly-molybdic acid metal that has a three-dimensional structure. The synthesis method is simple with high yield and reproducibility. The organic frame material having isopoly-molybdic acid metal shows high catalytic activity towards the ring-opening polymerization of lactide. The resulting polylactic acid has a weight average molecular weight exceeding 60,000.

Abstract: This application discloses a spatial positioning method and related apparatus, and a navigation stick.