518 citations · 883 across the 6 of their papers we have counts for
8 papers
CoBIT: A Contrastive Bi-directional Image-Text Generation Model
Haoxuan You, Mandy Guo, Zhecan Wang +3
The field of vision and language has witnessed a proliferation of pre-trained foundation models. Most existing methods are independently pre-trained with contrastive objective like…
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners
Shen Yan, Tao Zhu, Zirui Wang +5
We explore an efficient approach to establish a foundational video-text model. We present VideoCoCa that maximally reuses a pretrained image-text contrastive captioner (CoCa) model…
Exploiting Category Names for Few-Shot Classification with Vision-Language Models
Taihong Xiao, Zirui Wang, Liangliang Cao +3
Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks. Notably, many vision-language models build two encoder…
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh +14
We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compos…
CoCa: Contrastive Captioners are Image-Text Foundation Models
Jiahui Yu, Zirui Wang, Vijay Vasudevan +3
Exploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This pape…
Combined Scaling for Zero-shot Transfer Learning
Hieu Pham, Zihang Dai, Golnaz Ghiasi +9
We present a combined scaling method - named BASIC - that achieves 85.7% top-1 accuracy on the ImageNet ILSVRC-2012 validation set without learning from any labeled ImageNet exampl…