6 papers · 1 filter
Emu3.5: Native Multimodal Models are World Learners
Yufeng Cui, Honghao Chen, Haoge Deng +20
We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-tok…
Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewards
Honghao Chen, Xingzhou Lou, Xiaokun Feng +2
Chain of thought reasoning has demonstrated remarkable success in large language models, yet its adaptation to vision-language reasoning remains an open challenge with unclear best…
Emu3: Next-Token Prediction is All You Need
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo +22
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diff…
Generative Multimodal Models are In-Context Learners
Quan Sun, Yufeng Cui, Xiaosong Zhang +8
The human ability to easily solve multimodal tasks in context (i.e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggl…
CapsFusion: Rethinking Image-Text Data at Scale
Qiying Yu, Quan Sun, Xiaosong Zhang +5
Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute funda…
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Quan Sun, Jinsheng Wang, Qiying Yu +4
Scaling up contrastive language-image pretraining (CLIP) is critical for empowering both vision and multimodal models. We present EVA-CLIP-18B, the largest and most powerful open-s…