128 citations · 165 across the 4 of their papers we have counts for
4 papers
Emu: Generative Pretraining in Multimodality
Quan Sun, Qiying Yu, Yufeng Cui +7
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-mo…
Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline
Yangguang Li, Bin Huang, Zeren Chen +8
Recently, perception task based on Bird's-Eye View (BEV) representation has drawn more and more attention, and BEV representation is promising as the foundation for next-generation…
Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision
Yufeng Cui, Lichen Zhao, Feng Liang +2
Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigm to learn visual models from language supervision. While researchers continue to push the frontier of C…
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
Yangguang Li, Feng Liang, Lichen Zhao +5
Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferab…