Papers › Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
Biagio Brattoli, Joseph Tighe, Fedor Zhdanov, Pietro Perona, Krzysztof Chalupka
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 github.com/bbrattoli/ZeroShotVideoClassification.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Action Recognition | ActivityNet | E2E | Top-1 Accuracy | 26.6 | #5 of 5 | Archive leaderboard | report |
| Zero-Shot Action Recognition | HMDB51 | E2E | Top-1 Accuracy | 32.7 | #20 of 29 | Archive leaderboard | report |
| Zero-Shot Action Recognition | UCF101 | E2E | Top-1 Accuracy | 48 | #21 of 35 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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