Metric-Based Few-Shot Learning for Video Action Recognition
This work addresses the under-explored problem of few-shot learning for video classification, which is incremental as it applies existing methods to a new domain.
The paper tackles few-shot video action recognition by evaluating two-stream models with various video encoders and metric-based algorithms, achieving 84.2% accuracy on a standard test set and 59.4% on a challenging set for a 5-shot 5-way task.
In the few-shot scenario, a learner must effectively generalize to unseen classes given a small support set of labeled examples. While a relatively large amount of research has gone into few-shot learning for image classification, little work has been done on few-shot video classification. In this work, we address the task of few-shot video action recognition with a set of two-stream models. We evaluate the performance of a set of convolutional and recurrent neural network video encoder architectures used in conjunction with three popular metric-based few-shot algorithms. We train and evaluate using a few-shot split of the Kinetics 600 dataset. Our experiments confirm the importance of the two-stream setup, and find prototypical networks and pooled long short-term memory network embeddings to give the best performance as few-shot method and video encoder, respectively. For a 5-shot 5-way task, this setup obtains 84.2% accuracy on the test set and 59.4% on a special "challenge" test set, composed of highly confusable classes.