Pseudo-Active Vision For Improving Deep Visual Perception Through Neural Sensory Refinement

Active vision approaches hold the credentials for improving the accuracy of Deep Learning (DL) models for many challenging visual analysis tasks and varying environmental conditions. However, active vision approaches are typically closely tied to the underlying hardware, slowing down their adoption, while they typically increase the latency of perception systems, since sensory data must be recaptured. In this work, we propose a pseudo-active data refinement method that works by appropriately refining the sensory input, without having to reacquire the sensor data through traditional camera control approaches. The proposed method is fully differentiable and can be trained for the task at hand in an end-to-end fashion, while it can be directly deployed in a wide variety of systems, tasks and conditions. The effectiveness and robustness of the proposed method is demonstrated across a variety of tasks using two challenging datasets.