6 citations · 25 across the 19 of their papers we have counts for
16 papers · 1 filter
AR-CoPO: Align Autoregressive Video Generation with Contrastive Policy Optimization
Dailan He, Guanlin Feng, Xingtong Ge +5
Streaming autoregressive (AR) video generators combined with few-step distillation achieve low-latency, high-quality synthesis, yet remain difficult to align via reinforcement lear…
Neighbor GRPO: Contrastive ODE Policy Optimization Aligns Flow Models
Dailan He, Guanlin Feng, Xingtong Ge +6
Group Relative Policy Optimization (GRPO) has shown promise in aligning image and video generative models with human preferences. However, applying it to modern flow matching model…
High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning
Dailan He, Xiahong Wang, Shulun Wang +5
Face swapping aims to seamlessly transfer a source facial identity onto a target while preserving target attributes such as pose and expression. Diffusion models, known for their s…
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…
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…
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…
LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Hao Shao, Yuxuan Hu, Letian Wang +3
Despite significant recent progress in the field of autonomous driving, modern methods still struggle and can incur serious accidents when encountering long-tail unforeseen events…
Towards Large-scale Masked Face Recognition
Manyuan Zhang, Bingqi Ma, Guanglu Song +3
During the COVID-19 coronavirus epidemic, almost everyone is wearing masks, which poses a huge challenge for deep learning-based face recognition algorithms. In this paper, we will…
Decoupled DETR: Spatially Disentangling Localization and Classification for Improved End-to-End Object Detection
Manyuan Zhang, Guanglu Song, Yu Liu +1
The introduction of DETR represents a new paradigm for object detection. However, its decoder conducts classification and box localization using shared queries and cross-attention…