We propose a light-weight video frame interpolation algorithm. Our key innovation is an instance-level supervision that allows information to be learned from the high-resolution version of similar objects. Our experiment shows that the proposed method can generate state-of-art results across different datasets, with fractional computation resources (time and memory) with competing methods. Given two image frames, a cascade network creates an intermediate frame with 1) a flow-warping module that computes large bi-directional optical flow and creates an interpolated image via flow-based warping, followed by 2) an image synthesis module to make fine-scale corrections. In the learning stage, object detection proposals are generated on the interpolated image. Lower resolution objects are zoomed into, and the learning algorithms using an adversarial loss trained on high-resolution objects to guide the system towards the instance-level refinement corrects details of object shape and boundaries. As all our proposed network modules are fully convolutional, our proposed system can be trained end-to-end.

我们提出了一种轻量级视频帧插值算法，通过实例级监督进行学习，使用光流变形和图像合成模块生成中间图像，并结合低分辨率目标物体和对高分辨率目标物体进行对抗性逆向训练得到的损失，对图像中的细节进行实例级精细化纠正，最终得到与竞争方法相比更好的结果. 

Zoom-In-to-Check: 基于实例级别区分的视频插帧增强