Abstract: Methods, systems, and computer program products for implementing active learning in NLG tasks are provided herein. A computer-implemented method includes generating multiple natural language annotations associated with multiple items of unlabeled data by processing the unlabeled data using at least one artificial intelligence model; determining at least one quality score attributed to at least a portion of the multiple generated natural language annotations based at least in part on at least one quality metric; selecting at least one of the multiple natural language annotations and at least one corresponding item of the multiple items of unlabeled data based at least in part on the at least one determined quality score; and performing one or more automated actions based at least in part on the at least one selected natural language annotation.

Abstract: A table-to-text (T2T) generation model provides type control and semantic diversity. A method, system, and computer program product are configured to: train a model to generate one or more logic-type-specific natural language statements based on tabular data; in response to receiving a first input comprising first input data with a user-specified logic-type, the trained model generating a first logic-type-specific natural language statement based on the first input data and the user-specified logic-type; and in response to receiving a second input comprising second input data without a user-specified logic-type, the trained model generating plural second logic-type-specific natural language statements based on the second input data, wherein respective ones of the second logic-type-specific natural language statements are generated according to respective ones of plural predefined logic-types.

Abstract: An object detection system may generate regions of interest (ROIs) from an input image that can be processed by a wide range of object detectors. According to the techniques described herein, an image is processed by a light-weight neural network (e.g., a heatmap network) that outputs object center and object scale heat-maps. The heatmaps are processed to define ROIs that are likely to include objects. Overlapping ROIs are then merged to reduce the aggregate size of the ROIs, and merged ROIs are downscaled to a reduced set of pre-defined resolutions. Fully-convolutional, high-accuracy object detectors may then operate on the downscaled ROIs to output accurate detections at a fraction of the computations by operating on a reduced image. For example, fully-convolutional, high-accuracy object detectors may operate on a subset of the entire image (e.g., cropped images based on ROIs) thus reducing computations otherwise performed over the entire image.

Abstract: An object detection system may generate regions of interest (ROIs) from an input image that can be processed by a wide range of object detectors. According to the techniques described herein, an image is processed by a light-weight neural network (e.g., a heatmap network) that outputs object center and object scale heat-maps. The heatmaps are processed to define ROIs that are likely to include objects. Overlapping ROIs are then merged to reduce the aggregate size of the ROIs, and merged ROIs are downscaled to a reduced set of pre-defined resolutions. Fully-convolutional, high-accuracy object detectors may then operate on the downscaled ROIs to output accurate detections at a fraction of the computations by operating on a reduced image. For example, fully-convolutional, high-accuracy object detectors may operate on a subset of the entire image (e.g., cropped images based on ROIs) thus reducing computations otherwise performed over the entire image.