7 citations · 20 across the 11 of their papers we have counts for
12 papers
UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
Jiaqi Wang, Haoge Deng, Ting Pan +5
Uniform Discrete Diffusion Model (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with reinforcement learning remains l…
World Models as Group Actions
Zijie Wang, Wei Zhang, Weiming Zhang +4
Video world models have achieved strong visual realism, but this does not ensure that their dynamics are truly governed by actions. In this work, we argue that action faithfulness…
TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting
Fan Zhang, Shiming Fan, Hua Wang
Despite the recent success of large language models (LLMs) in time-series forecasting, most existing methods still adopt a Deep Synchronous Fusion strategy, where dense interaction…
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…
Uniform Discrete Diffusion with Metric Path for Video Generation
Haoge Deng, Ting Pan, Fan Zhang +8
Continuous-space video generation has advanced rapidly, while discrete approaches lag behind due to error accumulation and long-context inconsistency. In this work, we revisit disc…
End-to-End Vision Tokenizer Tuning
Wenxuan Wang, Fan Zhang, Yufeng Cui +5
Existing vision tokenization isolates the optimization of vision tokenizers from downstream training, implicitly assuming the visual tokens can generalize well across various tasks…
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…
Diffusion Feedback Helps CLIP See Better
Wenxuan Wang, Quan Sun, Fan Zhang +3
Contrastive Language-Image Pre-training (CLIP), which excels at abstracting open-world representations across domains and modalities, has become a foundation for a variety of visio…
DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception
Xiaotong Li, Fan Zhang, Haiwen Diao +3
Existing Multimodal Large Language Models (MLLMs) increasingly emphasize complex understanding of various visual elements, including multiple objects, text information, and spatial…
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…