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Luke Friedman

2 accepted papers

2024

Extending Video Masked Autoencoders to 128 frames

NeurIPS 2024poster

Video understanding has witnessed significant progress with recent video foundation models demonstrating strong performance owing to self-supervised pre-training objectives; Masked Autoencoders (MAE) being the design of choice. Nevertheless, the majority of prior works that leverage MAE pre-trainin…

Cited by 1SourcePDFScholar
2024

VideoPrism: A Foundational Visual Encoder for Video Understanding

ICML 2024poster

We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. We pretrain VideoPrism on a heterogeneous corpus containing 36M high-quality video-caption pairs and 582M video clips with noisy parallel text (e.g., ASR transcripts).…

Cited by 109SourcePDFScholar