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20222025
most citedTraining a Large Video Model on a Single Machine in a Day

5 citations · 10 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CV2025

Spherical Leech Quantization for Visual Tokenization and Generation

Yue Zhao, Hanwen Jiang, Zhenlin Xu +3

Non-parametric quantization has received much attention due to its efficiency on parameters and scalability to a large codebook. In this paper, we present a unified formulation of…

cs.CV2025

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Yue Zhao, Fuzhao Xue, Scott Reed +6

We introduce Quantized Language-Image Pretraining (QLIP), a visual tokenization method that combines state-of-the-art reconstruction quality with state-of-the-art zero-shot image u…

cs.CV2024

Image and Video Tokenization with Binary Spherical Quantization

Yue Zhao, Yuanjun Xiong, Philipp Krähenbühl

We propose a new transformer-based image and video tokenizer with Binary Spherical Quantization (BSQ). BSQ projects the high-dimensional visual embedding to a lower-dimensional hyp…

cs.CV2024★ 2 cited

Distilling Vision-Language Models on Millions of Videos

Yue Zhao, Long Zhao, Xingyi Zhou +9

The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply…

cs.CV2023★ 5 cited

Training a Large Video Model on a Single Machine in a Day

Yue Zhao, Philipp Krähenbühl

Videos are big, complex to pre-process, and slow to train on. State-of-the-art large-scale video models are trained on clusters of 32 or more GPUs for several days. As a consequenc…

cs.CV2022★ 2 cited

Learning Video Representations from Large Language Models

Yue Zhao, Ishan Misra, Philipp Krähenbühl +1

We introduce LaViLa, a new approach to learning video-language representations by leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be conditioned on visual…

cs.CV2022★ 1 cited

Real-time Online Video Detection with Temporal Smoothing Transformers

Yue Zhao, Philipp Krähenbühl

Streaming video recognition reasons about objects and their actions in every frame of a video. A good streaming recognition model captures both long-term dynamics and short-term ch…