6 citations · 25 across the 19 of their papers we have counts for
12 papers · 1 filter
VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video Face Swapping
Hao Shao, Shulun Wang, Yang Zhou +7
Video face swapping is becoming increasingly popular across various applications, yet existing methods primarily focus on static images and struggle with video face swapping becaus…
EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM
Zhuofan Zong, Dongzhi Jiang, Bingqi Ma +5
Significant achievements in personalization of diffusion models have been witnessed. Conventional tuning-free methods mostly encode multiple reference images by averaging their ima…
Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic Perspective
Jinouwen Zhang, Rongkun Xue, Yazhe Niu +4
Generative models, particularly diffusion models, have achieved remarkable success in density estimation for multimodal data, drawing significant interest from the reinforcement le…
SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
Yang Zhou, Hao Shao, Letian Wang +3
Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. However, the scarcity of…
MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
Jihao Liu, Xin Huang, Jinliang Zheng +5
This paper introduces MM-Instruct, a large-scale dataset of diverse and high-quality visual instruction data designed to enhance the instruction-following capabilities of large mul…
Enhancing Vision-Language Model with Unmasked Token Alignment
Jihao Liu, Jinliang Zheng, Boxiao Liu +2
Contrastive pre-training on image-text pairs, exemplified by CLIP, becomes a standard technique for learning multi-modal visual-language representations. Although CLIP has demonstr…
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models
Bingqi Ma, Zhuofan Zong, Guanglu Song +2
Large language models (LLMs) based on decoder-only transformers have demonstrated superior text understanding capabilities compared to CLIP and T5-series models. However, the parad…
UniZero: Generalized and Efficient Planning with Scalable Latent World Models
Yuan Pu, Yazhe Niu, Zhenjie Yang +3
Learning predictive world models is crucial for enhancing the planning capabilities of reinforcement learning (RL) agents. Recently, MuZero-style algorithms, leveraging the value e…
GLID: Pre-training a Generalist Encoder-Decoder Vision Model
Jihao Liu, Jinliang Zheng, Yu Liu +1
This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks. While self-supervised pre-training approac…
MoVA: Adapting Mixture of Vision Experts to Multimodal Context
Zhuofan Zong, Bingqi Ma, Dazhong Shen +5
As the key component in multimodal large language models (MLLMs), the ability of the visual encoder greatly affects MLLM's understanding on diverse image content. Although some lar…
SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
Yang Zhou, Hao Shao, Letian Wang +3
Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. Context information, suc…
Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning
Hao Shao, Shengju Qian, Han Xiao +5
Multi-Modal Large Language Models (MLLMs) have demonstrated impressive performance in various VQA tasks. However, they often lack interpretability and struggle with complex visual…