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VideoPrism is a new video encoder that improves video understanding through a unique training strategy, using a vast dataset (36 million high-quality video-caption pairs and 582 million video clips) for comprehensive learning.
VideoPrism is a new video encoder that improves video understanding through a unique training strategy, using a vast dataset (36 million high-quality video-caption pairs and 582 million video clips) for comprehensive learning.
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Key points:
Key points:
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* It employs a two-stage training approach, initially aligning video and text encoders, followed by an enhanced video-only masked autoencoding process to learn appearance and motion.
* It employs a two-stage training approach, initially aligning video and text encoders, followed by an enhanced video-only masked autoencoding process to learn appearance and motion.
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* It achieves superior performance in a wide array of tasks, such as general video understanding, zero-shot video-text retrieval, video captioning, QA, and computer vision for science, having top performance on 30 out of 33 benchmarks.
* It achieves superior performance in a wide array of tasks, such as general video understanding, zero-shot video-text retrieval, video captioning, QA, and computer vision for science, having top performance on 30 out of 33 benchmarks.
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Congrats to the authors for their work!
Congrats to the authors for their work!
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Paper: 
Paper: 
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https://huggingface.co/papers/2402.13217
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2402.13217
https://huggingface.co/papers/2402.13217
VideoPrism: A Foundational Visual Encoder for Video Understanding (2402.13217)
VideoPrism is a new video encoder that improves video understanding through a unique training strategy, using a vast dataset (36 million high-quality video-caption pairs and 582 million video clips) for comprehensive learning.

Key points:
* It employs a two-stage training approach, initially aligning video and text encoders, followed by an enhanced video-only masked autoencoding process to learn appearance and motion.
* It achieves superior performance in a wide array of tasks, such as general video understanding, zero-shot video-text retrieval, video captioning, QA, and computer vision for science, having top performance on 30 out of 33 benchmarks.

Congrats to the authors for their work!

Paper: https://huggingface.co/papers/2402.13217
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