Irrelevant Pixel

"Irrelevant pixels" research focuses on identifying and mitigating the computational cost of processing unnecessary image data in computer vision tasks. Current efforts involve developing methods to automatically detect and exclude these pixels, often using convolutional neural networks modified with techniques like focused convolutions or contrastive learning, and analyzing the relationship between pixel relevance and frequency aliasing in image processing. This work aims to improve the efficiency and energy consumption of computer vision systems, particularly for resource-constrained applications like mobile and IoT devices, by reducing computational load without sacrificing accuracy.