2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDIMT-1.3
Paper Title PSEUDO-ACTIVE VISION FOR IMPROVING DEEP VISUAL PERCEPTION THROUGH NEURAL SENSORY REFINEMENT
Authors Nikolaos Passalis, Anastasios Tefas, Aristotle University of Thessaloniki, Greece
SessionIMT-1: Computational Imaging Learning-based Models
LocationArea J
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Computational Imaging Methods and Models: Learning-Based Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.