Qualcomm Incorporated

Chun-Ting Huang

Lei Wang

Ning Bi

Zhen Wang

Xin Li

Xiaoliang Bai

Methods, systems, and apparatuses are provided to automatically determine whether an image is spoofed. For example, a computing device may obtain an image, and may execute a trained convolutional neural network to ingest elements of the image. Further, and based on the ingested elements of the image, the executed trained convolutional neural network generates an output map that includes a plurality of intensity values. In some examples, the trained convolutional neural network includes a plurality of down sampling layers, a plurality of up sampling layers, and a plurality of joint spatial and channel attention layers. Further, the computing device may determine whether the image is spoofed based on the plurality of intensity values. The computing device may also generate output data based on the determination of whether the image is spoofed, and may store the output data within a data repository.

Apparatus and methods for spoofing detection using machine learning processes

Systems and techniques are provided for performing video-based activity recognition. For example, a process can include extracting, using a first machine learning model, first one or more features from a first frame and second one or more features from a second frame. The first one or more features and the second one or more features are associated with a person driving a vehicle. The process can include processing, using a second machine learning model, the first one or more features and the second one or more features. The process can include determining, based on processing of the first one or more features and the second one or more features using the second machine learning model, at least one activity associated with the person driving the vehicle.

Video-based activity recognition

Methods, systems, and devices for object detection are described. A device may receive an image, and detect, via a first stage of a cascade neural network, object recognition information over one or more angular orientations during a first pass. The device may determine, via a second stage of the cascade neural network, a confidence score associated with one or more of the candidate object in the image, the candidate bounding box associated with the candidate object in the image, or one or more object features of the candidate object in the image, or an orientation of the candidate object in the image, or a combination thereof. The device may identify, via a third stage of the cascade neural network, whether to detect the object recognition information during a second pass based on the confidence score satisfying a threshold.

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San Diego, CA, United States of America

Chun-Ting Huang