Auto-TransRL: Autonomous Composition of Vision Pipelines for Robotic Perception

One of the most important parts of solving a vision task Our main contribution is to leverage the Transformer is to correctly identify the correct sequence of preprocessing Architecture [1] along with Deep Reinforcement Learning steps and the algorithms that would be most suitable for techniques to search the algorithmic space such that it restoring the image to a format that can be used for achieving can generalize well to the set of algorithms that were not the goal task. Preprocessing of images and videos plays used during training. In a nutshell, after the sequence of a very vital role in the performance of a computer vision preprocessing steps are decided, our framework performs a pipeline. Inappropriate choices of the preprocessing sequence knowledge based graph search over the algorithmic space at and algorithms can drastically hamper the performance of every stage of the pipeline and identifies the algorithms that the goal task. The preprocessing pipeline can have different would be well suited to complete the vision pipeline for a arrangements and the number of algorithms to choose from given input image. As our framework can retrieve algorithms are fairly large in number. As a result, there can exist multiple dynamically, it reduces the level of human intervention for such algorithmic configurations to choose from.