Efficient deep CNNs for cross-modal automated computer vision under time and space constraints

We present an automated computer vision architecture to handle video and image data using the same backbone networks. We show empirical results that lead us to adopt MOBILENETV2 as this backbone architecture. The paper demonstrates that neural architectures are transferable from images to videos through suitable preprocessing and temporal information fusion