Abstract: Obtaining a robustly trained image quality assessment machine learning model by training a machine learning model using a target image obtained by upscaling a downscaled reference image, an adversarial image obtained by upscaling a result of distorting the downscaled image, obtaining, from the machine learning model with first parameter values, a first result value for the target image relative to the reference image and a second result value for the adversarial image relative to the reference image, including, in the machine learning model, a result of subtracting a scaled gradient from the first parameter values, wherein the scaled gradient is a result of a product of a gradient of a loss function that is a maximum among zero and a result of adding a defined hinge loss value to difference between the result values.

Abstract: Systems and methods for image segmentation are described. Embodiments of the present disclosure receive an image depicting an object; generate image features for the image by performing a convolutional self-attention operation that outputs a plurality of attention-weighted values for a convolutional kernel applied at a position of a sliding window on the image; and generate label data that identifies the object based on the image features.

Abstract: A system and methods are disclosed for optimal format selection for video players based on visual quality. The method includes generating a plurality of reference transcoded versions of a reference video, obtaining quality scores for frames of the plurality of reference transcoded versions of the reference video, generating a first training input comprising a set of color attributes, spatial attributes, and temporal attributes of the frames of the reference video, and generating a first target output for the first training input, wherein the first target output comprises the quality scores for the frames of the plurality of reference transcoded versions of the reference video. The method further includes providing the training data to train a machine learning model on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output.

Abstract: Techniques for determining perceptual quality indicators of video content items are provided. In some embodiments, a system including one or more processors executes instructions to: receive a video content item comprising a plurality of frames; determine, using a first subnetwork of a deep neural network, a content quality indicator for each frame, wherein the content quality indicator corresponds to one or more semantic content indicators for each frame; determine, using a second subnetwork of the deep neural network, a video distortion indicator for each frame, wherein the video distortion indicator indicates a quality of the frame based on distortions contained within the frame; generate a quality level for each frame based on at least the content quality indicator and the video distortion indicator for the frame; and output an indication of quality for the video content item based on the quality level for each frame.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a progressive refinement network to refine alpha mattes generated utilizing a mask-guided matting neural network. In particular, the disclosed systems can use the matting neural network to process a digital image and a coarse guidance mask to generate alpha mattes at discrete neural network layers. In turn, the disclosed systems can use the progressive refinement network to combine alpha mattes and refine areas of uncertainty. For example, the progressive refinement network can combine a core alpha matte corresponding to more certain core regions of a first alpha matte and a boundary alpha matte corresponding to uncertain boundary regions of a second, higher resolution alpha matte. Based on the combination of the core alpha matte and the boundary alpha matte, the disclosed systems can generate a final alpha matte for use in image matting processes.

Abstract: Systems and methods for image segmentation are described. Embodiments of the present disclosure receive an image depicting an object; generate image features for the image by performing an atrous self-attention operation based on a plurality of dilation rates for a convolutional kernel applied at a position of a sliding window on the image; and generate label data that identifies the object based on the image features.

Abstract: A no-reference video assessment framework employs a multi-resolution input representation and a patch sampling mechanism on a video having multiple frames 302 to aggregate information across different granularities in spatial and temporal dimensions. The framework effectively models complex spacetime distortions that occur in user generated content-type videos. According to one aspect, the framework embeds video clips 302 as multi-resolution patch tokens using complementary modules. This includes a multi-resolution video embedding module 322, and a space-time factorized Transformer encoding module 324, 326. The multi-resolution video embedding module 322 is configured to encode multi-scale quality information in the video, capturing both global video composition from lower resolution frame and local details from larger resolution frames.

Abstract: Described herein are methods useful for quantification of on-target and off-target gene editing activity. The method comprises establishing a first consensus labeling in a genomic DNA in a first plurality of cells, which comprises introducing a first tag at a first plurality of pre-determined locations in the genomic DNA; performing the genomic editing assay with a nucleotide comprising a second tag; identifying locations of genome editing activities by detecting signals of the second tag in reference to signals of the first tag in the first consensus labeling; and calculating genome editing efficiencies at an on-target location or an off-target location.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”).

Abstract: The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi-scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.

Abstract: A training dataset that includes a first dataset and a second dataset is received. The first dataset includes a first subset of first videos corresponding to a first context and respective first ground truth quality scores of the first videos, and the second dataset includes a second subset of second videos corresponding to a second context and respective second ground truth quality scores of the second videos. A machine learning model is trained to predict the respective first ground truth quality scores and the respective second ground truth quality scores. Training the model includes training it to obtain a global quality score for one of the videos; and training it to map the global quality score to context-dependent predicted quality scores. The context-dependent predicted quality scores include a first context-dependent predicted quality score corresponding to the first context and a second context-dependent predicted quality score corresponding to the second context.

Abstract: An image processing system generates an image mask from an image. The image is processed by an object detector to identify a region having an object, and the region is classified based on an object type of the object. A masking pipeline is selected from a number of masking pipelines based on the classification of the region. The region is processed using the masking pipeline to generate a region mask. An image mask for the image is generated using the region mask.

Abstract: Video streams uploaded to a video hosting platform are transcoded using quality-normalized transcoding parameters dynamically selected using a learning model. Video frames of a video stream are processed using the learning model to determine bitrate and quality score pairs for some or all possible transcoding resolutions. The listing of bitrate and quality score pairs determined for each resolution is processed to determine a set of transcoding parameters for transcoding the video stream into each resolution. The bitrate and quality score pairs of a given listing may be processed using one or more predefined thresholds, which may, in some cases, refer to a weighted distribution of resolutions according to watch times of videos of the video hosting platform. The video stream is then transcoded into the various resolutions using the set of transcoding parameters selected for each resolution.

Abstract: Encoding using media compression and processing for machine-learning-based quality metrics includes generating encoded frame data by encoding a current frame from an input video using a neural-network-based video quality model, which includes identifying optimal encoding parameters for encoding a current block, wherein the optimal encoding parameters minimize a rate-distortion optimization cost function, which includes using a gradient value for the current block obtained from a neural-network-based video quality model generated gradient map obtained from the neural-network-based video quality model for the current frame, obtaining a restoration filtered reconstructed frame by restoration filtering a reconstructed frame, obtained by decoding the encoded frame data, using the neural-network-based video quality model generated gradient map obtained for the reconstructed frame.

Abstract: A trained model is retrained for video quality assessment and used to identify sets of adaptive compression parameters for transcoding user generated video content. Using transfer learning, the model, which is initially trained for image object detection, is retrained for technical content assessment and then again retrained for video quality assessment. The model is then deployed into a transcoding pipeline and used for transcoding an input video stream of user generated content. The transcoding pipeline may be structured in one of several ways. In one example, a secondary pathway for video content analysis using the model is introduced into the pipeline, which does not interfere with the ultimate output of the transcoding should there be a network or other issue. In another example, the model is introduced as a library within the existing pipeline, which would maintain a single pathway, but ultimately is not expected to introduce significant latency.

Abstract: Systems and methods for multi-modal representation learning are described. One or more embodiments provide a visual representation learning system trained using machine learning techniques. For example, some embodiments of the visual representation learning system are trained using cross-modal training tasks including a combination of intra-modal and inter-modal similarity preservation objectives. In some examples, the training tasks are based on contrastive learning techniques.

Abstract: The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi-scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.

Abstract: Methods, systems, and media for determining perceptual quality indicators of video content items are provided.

Abstract: A method for generating, for a video stream of a first spatial resolution and a first temporal resolution, a first reduced quality steam of a second spatial resolution and a second reduced-quality stream of a second temporal resolution. A first subset of STPs is sampled from the first reduced-quality stream and a second subset of STPs is sampled from the second reduced-quality stream. Using a machine learning model (MLM) the STPs are processed to identify a quality score for each quality-representative STPs that are representative of a quality of the video stream. One or more quality-improving actions for the video stream are identified using the quality scores of the quality-representative STPs.

Abstract: The present disclosure relates to a connector for a motor, a motor, and a vehicular compressor. The connector has a substrate and a plurality of stator terminals disposed on the substrate. The connector is further disposed with an insulation adhesive receiving part having a body detachably secured on the substrate; an opening for at least two stator terminals of the plurality of stator terminals to pass through; and a panel extending outwardly from a side of the body away from the substrate and circumferentially disposed along the opening for enclosing a space to receive the insulation adhesive. A first sealing portion is disposed on a side of the body proximate to the substrate, and a second sealing portion is disposed at a corresponding location of the substrate. The first sealing portion and the second sealing portion are sealed, press-fit and circumferentially disposed along the opening.