Video frame interpolation (VFI) is a fundamental vision task that aims to synthesize several frames between two consecutive original video images. Most algorithms aim to accomplish VFI by using only keyframes, which is an ill-posed problem since the keyframes usually do not yield any accurate precision about the trajectories of the objects in the scene. On the other hand, event-based cameras provide more precise information between the keyframes of a video. Some recent state-of-the-art event-based methods approach this problem by utilizing event data for better optical flow estimation to interpolate for video frame by warping. Nonetheless, those methods heavily suffer from the ghosting effect. On the other hand, some of kernel-based VFI methods that only use frames as input, have shown that deformable convolutions, when backed up with transformers, can be a reliable way of dealing with long-range dependencies. We propose event-based video frame interpolation with attention (E-VFIA), as a lightweight kernel-based method. E-VFIA fuses event information with standard video frames by deformable convolutions to generate high quality interpolated frames. The proposed method represents events with high temporal resolution and uses a multi-head self-attention mechanism to better encode event-based information, while being less vulnerable to blurring and ghosting artifacts; thus, generating crispier frames. The simulation results show that the proposed technique outperforms current state-of-the-art methods (both frame and event-based) with a significantly smaller model size.

翻译：视频帧插值是计算机视觉中的基础任务，旨在两个连续原始视频图像之间合成若干帧。大多数算法仅依赖关键帧完成插值，但这本质上是一个病态问题——关键帧通常无法精确描述场景中物体的运动轨迹。相比之下，事件相机能提供视频关键帧间更精确的时域信息。近年来，基于事件的最优方法通过利用事件数据改进光流估计，并采用图像扭曲方式实现视频帧插值。然而，这类方法普遍存在严重的鬼影效应。另一方面，仅使用帧输入的核函数方法表明，结合Transformer的可变形卷积能有效处理长程依赖关系。我们提出基于事件注意力机制的轻量化核函数方法E-VFIA（Event-based Video Frame Interpolation with Attention）。该方法通过可变形卷积将事件信息与标准视频帧融合，生成高质量插值帧。所提方案利用高时间分辨率的事件表示，并采用多头自注意力机制更有效地编码事件信息，同时显著降低模糊和鬼影伪影的干扰，从而生成更清晰的帧。仿真结果表明，该技术在显著减小模型规模的同时，其性能超越了当前最优的帧基和事件基方法。