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Few-shot learning aims to recognize novel classes from a few examples.Although significant progress has been made in the image domain, few-shot videoclassification is relatively unexplored. We argue that previous methodsunderestimate the importance of video feature learning and propose to learnspatiotemporal features using a 3D CNN. Proposing a two-stage approach thatlearns video features on base classes followed by fine-tuning the classifierson novel classes, we show that this simple baseline approach outperforms priorfew-shot video classification methods by over 20 points on existing benchmarks.To circumvent the need of labeled examples, we present two novel approachesthat yield further improvement. First, we leverage tag-labeled videos from alarge dataset using tag retrieval followed by selecting the best clips withvisual similarities. Second, we learn generative adversarial networks thatgenerate video features of novel classes from their semantic embeddings.Moreover, we find existing benchmarks are limited because they only focus on 5novel classes in each testing episode and introduce more realistic benchmarksby involving more novel classes, i.e. few-shot learning, as well as a mixtureof novel and base classes, i.e. generalized few-shot learning. The experimentalresults show that our retrieval and feature generation approach significantlyoutperform the baseline approach on the new benchmarks.

Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation