Sparse Perception

Sparse perception focuses on efficiently processing visual information by selectively attending to only the most relevant data points, rather than processing the entire image or scene. Current research emphasizes developing novel neural network architectures, such as SparseFormers and various adaptations of graph neural networks, that leverage sparse representations for tasks like autonomous driving and image compression. This approach offers significant advantages in terms of reduced computational cost, improved energy efficiency, and enhanced scalability, impacting fields ranging from robotics and computer vision to image processing and data compression.