Abstract: Apparatuses, systems, and techniques to use one or more neural networks to generate an upsampled version of one or more images based, at least in part, on a denoised version of said one or more images. At least one embodiment pertains to generating an upsampled high-resolution image from a noisy version and denoised version of a low-resolution image. At least one embodiment pertains to separating components of a low-resolution image before denoising an image.

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more circuits are to use one or more neural networks to adjust one or more pixel blending weights.

Abstract: Methods for composition aware image search refinement using relevance feedback. A method includes receiving, in response to user input, a relevance area within a plurality of image results, wherein the plurality of image results are provided for display on a user interface in response to a first search query using a plurality of spatial anchors in association with respective semantic datasets, and wherein the relevance area is associated with a target semantic dataset. The method also includes determining a mapping from the target semantic dataset to the plurality of spatial anchors, adjusting, based on the mapping, the semantic datasets associated with the plurality of spatial anchors, and providing for display, in the user interface, a plurality of refined image results in response to a second search query using the plurality of spatial anchors in association with the adjusted semantic datasets.

Abstract: A method for receiving a first text in a source language is provided. The method includes associating the first text in the source language with a first vector, selected from a plurality of vectors associated with the source language in an embedded set, and identifying a second vector that is closer to the first vector than a pre-selected threshold. The second vector is associated with a second text in a target language. The method includes associating the first text in the source language with the second text in the target language, wherein the embedded set includes a first image vector for a first image and a second image vector for a second image, and returning the second text in the target language, the second text being a translation of the first text from the source language to the target language. A system configured to perform the above method is also provided.

Abstract: A computer-implemented method is provided for retrieving an image from a user in a desired format and for detecting a compression efficiency for the image. When the compression efficiency is above a pre-selected threshold the computer-implemented method includes obtaining a saliency representation of the image, capturing a feature description of a non-salient portion of the image, flattening the non-salient portion in a new image, storing the new image in a selected format in a memory and storing a background descriptor for the image in the memory.

Abstract: A method for receiving a first text in a source language is provided. The method includes associating the first text in the source language with a first vector, selected from a plurality of vectors associated with the source language in an embedded set, and identifying a second vector that is closer to the first vector than a pre-selected threshold. The second vector is associated with a second text in a target language. The method includes associating the first text in the source language with the second text in the target language, wherein the embedded set includes a first image vector for a first image and a second image vector for a second image, and returning the second text in the target language, the second text being a translation of the first text from the source language to the target language. A system configured to perform the above method is also provided.

Abstract: A computer-implemented method is provided for retrieving an image from a user in a desired format and for detecting a compression efficiency for the image. When the compression efficiency is above a pre-selected threshold the computer-implemented method includes obtaining a saliency representation of the image, capturing a feature description of a non-salient portion of the image, flattening the non-salient portion in a new image, storing the new image in a selected format in a memory and storing a background descriptor for the image in the memory.