383 citations · 830 across the 12 of their papers we have counts for
14 papers
Can Visual Generation Expand What Unified Multimodal Models Know?
Wenkai Li, Wenyi Hong, Zhuoyi Yang +1
Unified multimodal models (UMMs) receive image-generation supervision that exposes them to visual information not fully documented in text. Can learning to generate images expand w…
GFD-OPD: Guidance-Folded On-Policy Distillation of Diffusion Models Across Scales
Zhenxing Zhang, Jiayan Teng, Wenxu Wu +6
On-policy distillation (OPD) has demonstrated two important capabilities in language models: compressing large teachers into smaller students and merging expert models into a singl…
MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
Wenyi Hong, Yean Cheng, Zhuoyi Yang +6
In recent years, vision language models (VLMs) have made significant advancements in video understanding. However, a crucial capability - fine-grained motion comprehension - remain…
VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation
Jiazheng Xu, Yu Huang, Jiale Cheng +19
Visual generative models have achieved remarkable progress in synthesizing photorealistic images and videos, yet aligning their outputs with human preferences across critical dimen…
CogVLM2: Visual Language Models for Image and Video Understanding
Wenyi Hong, Weihan Wang, Ming Ding +22
Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalit…
Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion Transformer
Zhuoyi Yang, Heyang Jiang, Wenyi Hong +5
Diffusion models have shown remarkable performance in image generation in recent years. However, due to a quadratic increase in memory during generating ultra-high-resolution image…
CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion
Wendi Zheng, Jiayan Teng, Zhuoyi Yang +6
Recent advancements in text-to-image generative systems have been largely driven by diffusion models. However, single-stage text-to-image diffusion models still face challenges, in…
CogVLM: Visual Expert for Pretrained Language Models
Weihan Wang, Qingsong Lv, Wenmeng Yu +13
We introduce CogVLM, a powerful open-source visual language foundation model. Different from the popular shallow alignment method which maps image features into the input space of…
Relay Diffusion: Unifying diffusion process across resolutions for image synthesis
Jiayan Teng, Wendi Zheng, Ming Ding +4
Diffusion models achieved great success in image synthesis, but still face challenges in high-resolution generation. Through the lens of discrete cosine transformation, we find the…
Parameter-Efficient Tuning Makes a Good Classification Head
Zhuoyi Yang, Ming Ding, Yanhui Guo +2
In recent years, pretrained models revolutionized the paradigm of natural language understanding (NLU), where we append a randomly initialized classification head after the pretrai…
GLM-130B: An Open Bilingual Pre-trained Model
Aohan Zeng, Xiao Liu, Zhengxiao Du +15
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good…
CogView: Mastering Text-to-Image Generation via Transformers
Ming Ding, Zhuoyi Yang, Wenyi Hong +8
Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding. We propose CogView, a 4…
Distributed High-dimensional Regression Under a Quantile Loss Function
Xi Chen, Weidong Liu, Xiaojun Mao +1
This paper studies distributed estimation and support recovery for high-dimensional linear regression model with heavy-tailed noise. To deal with heavy-tailed noise whose variance…
Distributed Inference for Linear Support Vector Machine
Xiaozhou Wang, Zhuoyi Yang, Xi Chen +1
The growing size of modern data brings many new challenges to existing statistical inference methodologies and theories, and calls for the development of distributed inferential ap…