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Creating a vision pipeline for different datasets to solve a computer visiontask is a complex and time consuming process. Currently, these pipelines aredeveloped with the help of domain experts. Moreover, there is no systematicstructure to construct a vision pipeline apart from relying on experience,trial and error or using template-based approaches. As the search space forchoosing suitable algorithms for achieving a particular vision task is large,human exploration for finding a good solution requires time and effort. Toaddress the following issues, we propose a dynamic and data-driven way toidentify an appropriate set of algorithms that would be fit for building thevision pipeline in order to achieve the goal task. We introduce a TransformerArchitecture complemented with Deep Reinforcement Learning to recommendalgorithms that can be incorporated at different stages of the vision workflow.This system is both robust and adaptive to dynamic changes in the environment.Experimental results further show that our method also generalizes well torecommend algorithms that have not been used while training and hencealleviates the need of retraining the system on a new set of algorithmsintroduced during test time.

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