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.
In: British Machine Vision Conference (BMVC)
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Introduces and evaluates triplet loss embedding techniques with repeated-term anchors, difficulty-balanced examples, and hard-example emphasis to improve neural ranking for Horn logic reasoning.
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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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High Quality Embeddings for Horn Logic Reasoning
Introduces and evaluates triplet loss embedding techniques with repeated-term anchors, difficulty-balanced examples, and hard-example emphasis to improve neural ranking for Horn logic reasoning.