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        <title>GRIZAL: Generative Prior-guided Zero-Shot Temporal Action Localization</title>
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        <namePart type="given">Gayatri</namePart>
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    <abstract>Zero-shot temporal action localization (TAL) aims to temporally localize actions in videos without prior training examples. To address the challenges of TAL, we offer GRIZAL, a model that uses multimodal embeddings and dynamic motion cues to localize actions effectively. GRIZAL achieves sample diversity by using large-scale generative models such as GPT-4 for generating textual augmentations and DALL-E for generating image augmentations. Our model integrates vision-language embeddings with optical flow insights, optimized through a blend of supervised and self-supervised loss functions. On ActivityNet, Thumos14 and Charades-STA datasets, GRIZAL greatly outperforms state-of-the-art zero-shot TAL models, demonstrating its robustness and adaptability across a wide range of video content. We will make all the models and code publicly available by open-sourcing them.</abstract>
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%0 Conference Proceedings
%T GRIZAL: Generative Prior-guided Zero-Shot Temporal Action Localization
%A Susladkar, Onkar
%A Deshmukh, Gayatri
%A Gorade, Vandan
%A Mittal, Sparsh
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F susladkar-etal-2024-grizal
%X Zero-shot temporal action localization (TAL) aims to temporally localize actions in videos without prior training examples. To address the challenges of TAL, we offer GRIZAL, a model that uses multimodal embeddings and dynamic motion cues to localize actions effectively. GRIZAL achieves sample diversity by using large-scale generative models such as GPT-4 for generating textual augmentations and DALL-E for generating image augmentations. Our model integrates vision-language embeddings with optical flow insights, optimized through a blend of supervised and self-supervised loss functions. On ActivityNet, Thumos14 and Charades-STA datasets, GRIZAL greatly outperforms state-of-the-art zero-shot TAL models, demonstrating its robustness and adaptability across a wide range of video content. We will make all the models and code publicly available by open-sourcing them.
%U https://aclanthology.org/2024.emnlp-main.1061
%P 19046-19059

Markdown (Informal)

[GRIZAL: Generative Prior-guided Zero-Shot Temporal Action Localization](https://aclanthology.org/2024.emnlp-main.1061) (Susladkar et al., EMNLP 2024)

• GRIZAL: Generative Prior-guided Zero-Shot Temporal Action Localization (Susladkar et al., EMNLP 2024)

ACL

• Onkar Susladkar, Gayatri Deshmukh, Vandan Gorade, and Sparsh Mittal. 2024. GRIZAL: Generative Prior-guided Zero-Shot Temporal Action Localization. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 19046–19059, Miami, Florida, USA. Association for Computational Linguistics.