Abstract: In various examples, techniques for optimizing object detection models are described herein. Systems and methods are disclosed that process sensor data using a backbone of a machine learning model(s) in order to generate feature maps at different resolutions. The systems and methods then use the machine learning model(s) to generate a vector based at least in part on one or more of the feature maps. For example, if the backbone generates four feature maps, then the machine learning model(s) may generate the vector using two feature maps from the four feature maps. The systems and methods then process the vector using a transformer of the machine learning model(s) in order to generate data representing a class label(s) for an object(s) depicted by an image represented by the sensor data and/or a location(s) of the object(s) within the image.

Abstract: In various examples, techniques for optimizing object detection models are described herein. Systems and methods are disclosed that process sensor data using a backbone of a machine learning model(s) in order to generate feature maps at different resolutions. The systems and methods then use the machine learning model(s) to generate a vector based at least in part on one or more of the feature maps. For example, if the backbone generates four feature maps, then the machine learning model(s) may generate the vector using two feature maps from the four feature maps. The systems and methods then process the vector using a transformer of the machine learning model(s) in order to generate data representing a class label(s) for an object(s) depicted by an image represented by the sensor data and/or a location(s) of the object(s) within the image.

Abstract: In various examples, techniques for optimizing object detection models are described herein. Systems and methods are disclosed that process sensor data using a backbone of a machine learning model(s) in order to generate feature maps at different resolutions. The systems and methods then use the machine learning model(s) to generate a vector based at least in part on one or more of the feature maps. For example, if the backbone generates four feature maps, then the machine learning model(s) may generate the vector using two feature maps from the four feature maps. The systems and methods then process the vector using a transformer of the machine learning model(s) in order to generate data representing a class label(s) for an object(s) depicted by an image represented by the sensor data and/or a location(s) of the object(s) within the image.

Abstract: A method for detecting moving objects in video frames, an apparatus and a non-transitory computer-readable storage medium thereof are provided. The method includes that: an encoder in a 3-dimenional (3D) separable convolutional neural network with multi-input multi-output (3DS_MM) receives a first input including multiple video frames, where the encoder includes a plurality of encoder layers including 3D separable convolutional neural network (CNN) layers; the encoder generates a first encoder output; and a decoder in the 3DS_MM receives the first encoder output and generates a first output including multiple first binary masks related to the first input, where the decoder includes a plurality of decoder layers comprising 3D separable transposed CNN layers.