论文标题
TAEN：暂时意识的嵌入网络，用于几次动作识别
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition
论文作者
论文摘要
新类实体的分类需要收集和注释数百或数千个样本通常是昂贵的。很少有学习建议仅使用几个示例学习学习新课程。只有少数研究解决了关于时空模式（例如视频）的挑战。在本文中，我们介绍了暂时的意识嵌入网络（TAEN），以进行几次动作识别，该识别学会在度量空间中作为轨迹表示动作，以传达短期语义和操作零件之间的长期连接性。我们证明了TAEN对两项射击任务，视频分类和时间动作检测的有效性，并评估了我们在动力学400和ActivityNet 1.2少数基准测试的方法上。通过培训仅几个完全连接的层，我们就可以在少数拍摄视频分类和时间检测任务上取得可比的结果，同时在某些情况下达到最先进的结果。
Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to classify new classes using just a few examples. Only a small number of studies address the challenge of few-shot learning on spatio-temporal patterns such as videos. In this paper, we present the Temporal Aware Embedding Network (TAEN) for few-shot action recognition, that learns to represent actions, in a metric space as a trajectory, conveying both short term semantics and longer term connectivity between action parts. We demonstrate the effectiveness of TAEN on two few shot tasks, video classification and temporal action detection and evaluate our method on the Kinetics-400 and on ActivityNet 1.2 few-shot benchmarks. With training of just a few fully connected layers we reach comparable results to prior art on both few shot video classification and temporal detection tasks, while reaching state-of-the-art in certain scenarios.
