CEZSAR uses contrastive learning to align video and sentence embeddings with automatic negative sampling, claiming state-of-the-art zero-shot action recognition on UCF-101 and Kinetics-400.
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NTGA is the first clean-label generalization attack under black-box settings but is vulnerable to adversarial training and image transformations, with newer attacks outperforming it.
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CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition
CEZSAR uses contrastive learning to align video and sentence embeddings with automatic negative sampling, claiming state-of-the-art zero-shot action recognition on UCF-101 and Kinetics-400.
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SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions
NTGA is the first clean-label generalization attack under black-box settings but is vulnerable to adversarial training and image transformations, with newer attacks outperforming it.