Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
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We assume that a client, a target application with its own small labeled dataset, is only interested in fetching a subset of the server’s data that is most relevant to its own target domain.
We propose neural architectures that learn to disentangle an RGB-D video steam into camera motion and 3D scene appearance, and capture the latter into 3D feature representations that can be trained end-to-end with 3D object detection and object motion forecasting.
Offset regression is a standard method for spatial localization in many vision tasks, including human pose estimation, object detection, and instance segmentation.
Human observers can learn to recognize new categories of objects from a handful of examples, yet doing so with machine perception remains an open challenge.
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.
We explore the idea of compositional set embeddings that can be used to infer not just a single class, but the set of classes associated with the input data (e. g., image, video, audio signal).