We propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. Our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. We develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features.
Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition.
In this work, we propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras under various real-world conditions, especially from sparse and noisy events generated under non-uniform motion.
In this paper, we propose a novel hierarchical knowledge distillation framework that can fully utilize multi-modal / multi-view information during training to facilitate knowledge transfer, enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals.
This study proposes an event filter-based phase correlation template match (EF-PCTM) method, including its optimal design procedure, to measure micro-vibrations using an event camera. In this study, event filter is designed using an infinite impulse response filter and genetic algorithm, and a cost function is defined to improve the performance of EF-PCTM.
