1 citations · 1 across the 4 of their papers we have counts for
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WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens
Jian Yang, Dacheng Yin, Xiaoxuan He +6
Recent progress in multimodal large language models (MLLMs) has highlighted the challenge of efficiently bridging pre-trained Vision-Language Models (VLMs) with Diffusion Models. W…
Towards Sequence Modeling Alignment between Tokenizer and Autoregressive Model
Pingyu Wu, Kai Zhu, Yu Liu +6
Autoregressive image generation aims to predict the next token based on previous ones. However, this process is challenged by the bidirectional dependencies inherent in conventiona…
VanGogh: A Unified Multimodal Diffusion-based Framework for Video Colorization
Zixun Fang, Zhiheng Liu, Kai Zhu +5
Video colorization aims to transform grayscale videos into vivid color representations while maintaining temporal consistency and structural integrity. Existing video colorization…
Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning
Fan Lu, Wei Wu, Kecheng Zheng +7
Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have dev…
Improved Video VAE for Latent Video Diffusion Model
Pingyu Wu, Kai Zhu, Yu Liu +4
Variational Autoencoder (VAE) aims to compress pixel data into low-dimensional latent space, playing an important role in OpenAI's Sora and other latent video diffusion generation…