Abstract: An example method for camera processing includes detecting a first region of interest for image content captured with a primary camera, detecting a second region of interest for image content captured with a secondary camera, comparing the first region of interest and the second region of interest, determining a first lens position for a secondary camera lens of the secondary camera based on an autofocus process for the secondary camera, determining a second lens position for the secondary camera lens of the secondary camera based on a determined lens position for a primary camera lens of the primary camera, determining a lens position for the secondary camera lens based on the first lens position or the second lens position based at least in part on the comparison, and causing the secondary camera to capture images with the secondary camera lens at the determined lens position.

Abstract: A processor may be configured to receive an image having lens distortion from a camera module and may determine one or more configuration settings from the image based on the lens distortion. In some examples, the processor may perform lens distortion correction on the image prior to determining the one or more configuration settings. In other examples, the processor may determine configuration settings based on distorted grid cells of the image having lens distortion, wherein the distorted grid cells are defined by the lens distortion. In other examples, the processor may determine initial configuration statistics values from the image having the lens distortion and then adjust the initial configuration statistics values based on the lens distortion.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for federal learning are disclosed. One exemplary method may include receiving, by a client device from a server device, model parameters of a global machine learning model being collaboratively trained by the client device and the server device; constructing, by the client device, a local machine learning model based on the model parameters of the global machine learning model, wherein the local machine learning model comprises two branches corresponding to two loss functions; training, by the client device, the local machine learning model based on local training data, wherein the training comprises updating the model parameters to minimize a first loss function of the two loss functions and maximize a second loss function of the two loss functions; and sending, by the client device, the updated model parameters back to the server device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying images responsive to a search phrase are disclosed. In one aspect, a method includes identifying a set of responsive images for a search phrase that includes two or more terms. Interaction rankings are determined for images in the set of responsive images. Two or more sub-queries are created based on the search phrase. Sub-query model rankings are determined for images in the set of responsive images. A search phrase score is determined for the image relevance model. Based on the search phrase scores for the sub-queries, one of the sub-query models is selected as a model for the search phrase.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying images responsive to a search phrase are disclosed. In one aspect, a method includes identifying a set of responsive images for a search phrase that includes two or more terms. Interaction rankings are determined for images in the set of responsive images. Two or more sub-queries are created based on the search phrase. Sub-query model rankings are determined for images in the set of responsive images. A search phrase score is determined for the image relevance model. Based on the search phrase scores for the sub-queries, one of the sub-query models is selected as a model for the search phrase.