Papers by Sparsh Mittal

1 papers
GRIZAL: Generative Prior-guided Zero-Shot Temporal Action Localization (2024.emnlp-main)

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Challenge: Existing methods to temporally localize videos without prior training examples are lacking due to the complexity of annotated videos.
Approach: They propose a model that uses multimodal embeddings and dynamic motion cues to localize actions effectively.
Outcome: GRIZAL outperforms state-of-the-art zero-shot temporal action localization models on ActivityNet, Thumos14 and Charades-STA datasets.

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