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Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications

Brattoli, Biagio and Tighe, Joseph and Zhdanov, Fedor and Perona, Pietro and Chalupka, Krzysztof (2020) Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications. . (Unpublished)

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Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) proposes one solution to this problem. ZSL trains a model once, and generalizes to new tasks whose classes are not present in the training dataset. We propose the first end-to-end algorithm for ZSL in video classification. Our training procedure builds on insights from recent video classification literature and uses a trainable 3D CNN to learn the visual features. This is in contrast to previous video ZSL methods, which use pretrained feature extractors. We also extend the current benchmarking paradigm: Previous techniques aim to make the test task unknown at training time but fall short of this goal. We encourage domain shift across training and test data and disallow tailoring a ZSL model to a specific test dataset. We outperform the state-of-the-art by a wide margin. Our code, evaluation procedure and model weights are available at this http URL.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper ItemCode
Perona, Pietro0000-0002-7583-5809
Chalupka, Krzysztof0000-0002-1225-2112
Additional Information:We thank Amazon for generously supporting the project, and Alina Roitberg for a productive discussion on the evaluation protocol.
Funding AgencyGrant Number
Record Number:CaltechAUTHORS:20200526-133440422
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103459
Deposited By: Tony Diaz
Deposited On:26 May 2020 20:39
Last Modified:26 May 2020 20:39

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