Abstract: Approaches presented herein provide for semantic data matching, as may be useful for selecting data from a large unlabeled dataset to train a neural network. For an object detection use case, such a process can identify images within an unlabeled set even when an object of interest represents a relatively small portion of an image or there are many other objects in the image. A query image can be processed to extract image features or feature maps from only one or more regions of interest in that image, as may correspond to objects of interest. These features are compared with images in an unlabeled dataset, with similarity scores being calculated between the features of the region(s) of interest and individual images in the unlabeled set. One or more highest scored images can be selected as training images showing objects that are semantically similar to the object in the query image.

Abstract: Approaches presented herein provide for semantic data matching, as may be useful for selecting data from a large unlabeled dataset to train a neural network. For an object detection use case, such a process can identify images within an unlabeled set even when an object of interest represents a relatively small portion of an image or there are many other objects in the image. A query image can be processed to extract image features or feature maps from only one or more regions of interest in that image, as may correspond to objects of interest. These features are compared with images in an unlabeled dataset, with similarity scores being calculated between the features of the region(s) of interest and individual images in the unlabeled set. One or more highest scored images can be selected as training images showing objects that are semantically similar to the object in the query image.

Abstract: Approaches presented herein provide for semantic data matching, as may be useful for selecting data from a large unlabeled dataset to train a neural network. For an object detection use case, such a process can identify images within an unlabeled set even when an object of interest represents a relatively small portion of an image or there are many other objects in the image. A query image can be processed to extract image features or feature maps from only one or more regions of interest in that image, as may correspond to objects of interest. These features are compared with images in an unlabeled dataset, with similarity scores being calculated between the features of the region(s) of interest and individual images in the unlabeled set. One or more highest scored images can be selected as training images showing objects that are semantically similar to the object in the query image.

Abstract: Techniques and systems are described herein for determining dynamic lighting for objects in images. Using such techniques and systems, a lighting condition of one or more captured images can be adjusted. Techniques and systems are also described herein for determining depth values for one or more objects in an image. In some cases, the depth values (and the lighting values) can be determined using only a single camera and a single image, in which case one or more depth sensors are not needed to produce the depth values.

Abstract: Techniques and systems are described herein for determining dynamic lighting for objects in images. Using such techniques and systems, a lighting condition of one or more captured images can be adjusted. Techniques and systems are also described herein for determining depth values for one or more objects in an image. In some cases, the depth values (and the lighting values) can be determined using only a single camera and a single image, in which case one or more depth sensors are not needed to produce the depth values.

Abstract: Techniques and systems are provided for processing one or more low light images. For example, a short exposure image associated with one or more shutter speeds can be obtained. A long exposure image is also obtained, which is captured using a slower shutter speed than the one or more shutter speeds associated with the short exposure image. An output image can be generated by mapping color information from the long exposure image to the short exposure image.

Abstract: Techniques and systems are described herein for determining dynamic lighting for objects in images. Using such techniques and systems, a lighting condition of one or more captured images can be adjusted. Techniques and systems are also described herein for determining depth values for one or more objects in an image. In some cases, the depth values (and the lighting values) can be determined using only a single camera and a single image, in which case one or more depth sensors are not needed to produce the depth values.

Abstract: Techniques and systems are provided for processing one or more low light images. For example, a short exposure image associated with one or more shutter speeds can be obtained. A long exposure image is also obtained, which is captured using a slower shutter speed than the one or more shutter speeds associated with the short exposure image. An output image can be generated by mapping color information from the long exposure image to the short exposure image.

Abstract: Techniques and systems are described herein for determining dynamic lighting for objects in images. Using such techniques and systems, a lighting condition of one or more captured images can be adjusted. Techniques and systems are also described herein for determining depth values for one or more objects in an image. In some cases, the depth values (and the lighting values) can be determined using only a single camera and a single image, in which case one or more depth sensors are not needed to produce the depth values.