Abstract: Methods, systems, and techniques for image categorization using a visual language model. A set of images is clustered into clusters respectively corresponding to differently categorized objects. Names are respectively assigned to the clusters, and image captions are respectively generated for the clusters using the names. The image captions and respective images represent image-text pairs. Those image-text pairs are input to the visual language model as context for a query. The query is then input to the visual language model. The query includes a request to categorize a query image of a class represented in the context. In response to the query, the visual language model performs an open-ended generative categorization of the query image.

Abstract: This disclosure relates to improved sketch-based image retrieval (SBIR) techniques. The SBIR techniques utilize a neural network architecture to train a domain migration function and a hashing function. The domain migration function is configured to transform sketches into synthetic images, and the hashing function is configured to generate hash codes from synthetic images and authentic images in a manner that preserves semantic consistency across the sketch and image domains. The hash codes generated from the synthetic images can be used for accurately identifying and retrieving authentic images corresponding to sketch queries, or vice versa.

Abstract: Methods, systems, and techniques for kernel continuing learning. A dataset is obtained that corresponds to a classification task. Feature extraction is performed on the dataset using an artificial neural network. A kernel is constructed using features extracted during that feature extraction for use in performing the classification task. More particularly, during training, a coreset dataset corresponding to the classification task is saved; and during subsequent inference, the coreset dataset is retrieved and used to construct a task-specific kernel for classification.

Abstract: This disclosure relates to improved techniques for generating images from raw image sensor data captured in low-light conditions without the use of flash photography. The techniques described herein utilize a neural network architecture to transform the raw image sensor data into well-exposed images. The neural network architecture can be trained using a multi-criterion loss function that jointly models both pixel-level and feature-level properties of the images. The images output by the neural network architecture can be provided to a contrast correction module that enhances the contrast of the images.

Abstract: This disclosure relates to improved techniques for performing multi-view image clustering. The techniques described herein utilize machine learning functions to optimize the image clustering process. Multi-view features are extracted from a collection of images. A machine learning function is configured to jointly learn a fused binary representation that combines the multi-view features and one or more binary cluster structures that can be used to partition the images. A clustering function utilizes the fused binary representation and the one or more binary cluster structures to generate one or more image clusters based on the collection of images.

Abstract: This disclosure relates to improved techniques for performing computer vision functions including motion deblurring functions. The techniques described herein utilize a neural network architecture to perform these functions. The neural network architecture can include a human-aware attention model that is able to distinguish between foreground human objects and background portions of degraded images affected by motion blur. The neural network architecture further includes an encoder-decoder network that separately performs motion deblurring functions on foreground and background portions of degraded images, and reconstructs enhanced images corresponding to the degraded images.

Abstract: This disclosure relates to improved techniques for performing computer vision functions on medical images, including object segmentation functions for identifying medical objects in the medical images and grading functions for determining severity labels for medical conditions exhibited in the medical images. The techniques described herein utilize a neural network architecture to perform these and other functions. The neural network architecture can be trained, at least in part, using semi-supervised learning techniques that enable the neural network architecture to accurately perform the object segmentation and grading functions despite limited availability of pixel-level annotation information.

Abstract: This disclosure relates to improved sketch-based image retrieval (SBIR) techniques. The SBIR techniques utilize an architecture comprising three interconnected neural networks to enable zero-shot image recognition and retrieval based on free-hand sketches. Zero-shot learning may be implemented to retrieve one or more images corresponding to the sketches without prior training on all categories of the sketches. The neural network architecture may do so, at least in part, by training encoder hashing functions to mitigate heterogeneity of sketches and images, and by applying semantic knowledge that is learned during a limited training phase to unknown categories.

Abstract: This disclosure relates to improved vehicle re-identification techniques. The techniques described herein utilize artificial intelligence (AI) and machine learning functions to re-identify vehicles across multiple cameras. Vehicle re-identification can be performed using an image of the vehicle that is captured from any single viewpoint. Attention maps may be generated that identify regions of the vehicle that include visual patterns that overlap between the viewpoint of the captured image and one or more additional viewpoints. The attention maps are used to generate a multi-view representation of the vehicle that provides a global view of the vehicle across multiple viewpoints. The multi-view representation of the vehicle can then be compared to previously captured image data to perform vehicle re-identification.