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Generalized Many-Way Few-Shot Video Classification

MPS-Authors
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Xian,  Yongqin
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Schiele,  Bernt
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Akata,  Zeynep
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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arXiv:2007.04755.pdf
(Preprint), 5MB

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Citation

Xian, Y., Korbar, B., Douze, M., Schiele, B., Akata, Z., & Torresani, L. (2020). Generalized Many-Way Few-Shot Video Classification. Retrieved from https://arxiv.org/abs/2007.04755.


Cite as: https://hdl.handle.net/21.11116/0000-0007-80D7-5
Abstract
Few-shot learning methods operate in low data regimes. The aim is to learn
with few training examples per class. Although significant progress has been
made in few-shot image classification, few-shot video recognition is relatively
unexplored and methods based on 2D CNNs are unable to learn temporal
information. In this work we thus develop a simple 3D CNN baseline, surpassing
existing methods by a large margin. To circumvent the need of labeled examples,
we propose to leverage weakly-labeled videos from a large dataset using tag
retrieval followed by selecting the best clips with visual similarities,
yielding further improvement. Our results saturate current 5-way benchmarks for
few-shot video classification and therefore we propose a new challenging
benchmark involving more classes and a mixture of classes with varying
supervision.