论文标题
改进质子的几个镜头视频对象识别：轨道挑战的获胜者2022
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
论文作者
论文摘要
在这项工作中，我们介绍了轨道上的几乎没有视频对象识别挑战2022的获胜解决方案。建立在Protonet基线的基础上，通过三种有效的技术，我们的方法的性能得到了提高。这些技术包括嵌入适应性，统一的视频剪辑采样器和无效的框架检测。此外，我们重新开始并重新实施官方代码库，以鼓励模块化，兼容性和提高性能。我们的实施加速了培训和测试中的数据加载。
In this work, we present the winning solution for ORBIT Few-Shot Video Object Recognition Challenge 2022. Built upon the ProtoNet baseline, the performance of our method is improved with three effective techniques. These techniques include the embedding adaptation, the uniform video clip sampler and the invalid frame detection. In addition, we re-factor and re-implement the official codebase to encourage modularity, compatibility and improved performance. Our implementation accelerates the data loading in both training and testing.
