Abstract: A method receives first and second data generated from a first and second domains including first and second set of objects, receiving first class labels for each of the first set of objects, and receiving second class labels for each of the second set of objects. The method generates a training dataset by augmenting the first data and corresponding first class labels, and locally updating neural network parameters of a model based on the training dataset. The method generates a validation dataset by augmenting the second data and corresponding second class labels, and globally updating the neural network parameters of the model based on the validation dataset. The method also generates multiple target labels for target data generated from a target domain including a third set of objects after globally updating the neural network parameters of the model based on the validation dataset.

Abstract: In some aspects, a device may obtain point data from a lidar scanner. The point data may be associated with an angular subrange of a polar grid of the lidar scanner. The device may cause a transformer model to process the point data to identify a set of points based at least in part on the angular subrange, analyze the set of points based at least in part on a polar distance between the set of points and an origin of the polar grid, and indicate whether the set of points is associated with an object. The device may perform an action based at least in part on whether the transformer model indicates that the set of points is associated with the object. Numerous other aspects are described.

Abstract: Certain aspects of the present disclosure provide techniques for performing tabular convolution, including performing a tabularization operation on input data to generate a tabularized representation of the input data and performing a convolution operation using the tabularized representation of the input data to generate a convolution output.