This invention discloses a multimodal data heterogeneous Transformer-based asset recognition method, system, and device, the method including: collecting various-modal information of an asset, including text information and image information; building an ALBERT model, a ViT model, and a CLIP model; by the ALBERT model, extracting a text information feature; by the ViT model, extracting an image information feature; by the CLIP model, extracting image-text matching information feature; by different channels, applying asset type recognition to information in different modalities; outputting classification information from the different channels; by the CLIP model, generating asset void information; and discriminatively fusing the classification information from the different channels with the matching degree between the image information and the text information obtained by the CLIP model, and outputting final asset class information. This invention realizes comprehensive discrimination by drawing from multiple modalities to improve the accuracy of asset recognition.

A method of authorizing a device action includes accessing a first baseline model that represents image characteristics of an authorized first user or object. The first baseline model is used as a basis for selecting a first number of sensing structures of a camera image sensor, wherein the first number of sensing structures of the camera image sensor is less than a total number of sensing structures of the camera image sensor. The selected first number of sensing structures of the camera image sensor is activated and a first image sensed by the activated first number of sensing structures is obtaining. The first image is compared with the first baseline model. A next round of authorization processing is activated when an amount of correlation between the first image and the first baseline model satisfies a threshold correlation amount.