Search Results for author: David Junhao Zhang

Found 9 papers, 6 papers with code

Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

1 code implementation27 Sep 2023 David Junhao Zhang, Jay Zhangjie Wu, Jia-Wei Liu, Rui Zhao, Lingmin Ran, YuChao Gu, Difei Gao, Mike Zheng Shou

In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation.

Dataset Condensation via Generative Model

no code implementations14 Sep 2023 David Junhao Zhang, Heng Wang, Chuhui Xue, Rui Yan, Wenqing Zhang, Song Bai, Mike Zheng Shou

Dataset condensation aims to condense a large dataset with a lot of training samples into a small set.

Dataset Condensation

Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks

no code implementations13 Aug 2023 David Junhao Zhang, Mutian Xu, Chuhui Xue, Wenqing Zhang, Xiaoguang Han, Song Bai, Mike Zheng Shou

Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy.

Contrastive Learning Image Classification +2

Too Large; Data Reduction for Vision-Language Pre-Training

2 code implementations ICCV 2023 Alex Jinpeng Wang, Kevin Qinghong Lin, David Junhao Zhang, Stan Weixian Lei, Mike Zheng Shou

Specifically, TL;DR can compress the mainstream VLP datasets at a high ratio, e. g., reduce well-cleaned CC3M dataset from 2. 82M to 0. 67M ($\sim$24\%) and noisy YFCC15M from 15M to 2. 5M ($\sim$16. 7\%).

Making Vision Transformers Efficient from A Token Sparsification View

1 code implementation CVPR 2023 Shuning Chang, Pichao Wang, Ming Lin, Fan Wang, David Junhao Zhang, Rong Jin, Mike Zheng Shou

In this work, we propose a novel Semantic Token ViT (STViT), for efficient global and local vision transformers, which can also be revised to serve as backbone for downstream tasks.

Image Classification Instance Segmentation +4

Label-Efficient Online Continual Object Detection in Streaming Video

1 code implementation ICCV 2023 Jay Zhangjie Wu, David Junhao Zhang, Wynne Hsu, Mengmi Zhang, Mike Zheng Shou

Remarkably, with only 25% annotated video frames, our method still outperforms the base CL learners, which are trained with 100% annotations on all video frames.

Continual Learning Hippocampus +2

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