3 accepted papers
Event cameras are ideal for visual place recognition (VPR) in challenging environments due to their high temporal resolution and high dynamic range. However, existing methods convert sparse events into dense frame-like representations for Artificial Neural Networks (ANNs), ignoring event sparsity an
Monocular depth estimation based on event cameras has attracted widespread attention of researchers as event-cameras, with their high dynamic range and temporal resolution, can offer enhanced environmental perception ability under challenging lighting conditions. However, due to the inherent texture
The increasing complexity and parameter count of Convolutional Neural Networks (CNNs) and Transformers pose challenges in terms of computational efficiency and resource demands. Pruning has been identified as an effective strategy to address these challenges by removing redundant elements such as ne…