7 citations · 17 across the 5 of their papers we have counts for
5 papers
FAIL: Flow Matching Adversarial Imitation Learning for Image Generation
Yeyao Ma, Chen Li, Xiaosong Zhang +2
Post-training of flow matching models-aligning the output distribution with a high-quality target-is mathematically equivalent to imitation learning. While Supervised Fine-Tuning m…
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…
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…
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…