59 citations · 138 across the 21 of their papers we have counts for
22 papers
PDMD: Projected Distribution Matching Distillation for Video Diffusion Models
Zimo Wang, Junkun Yuan, Angtian Wang +9
Modern video diffusion models require tens of denoising evaluations over long spatiotemporal token sequences. Distribution Matching Distillation (DMD) reduces the number of functio…
Follow-Your-Preference++: Rethinking Preference Alignment for Image Inpainting
Junkun Yuan, Yutao Shen, Toru Aonishi +2
We study preference alignment for image inpainting. Rather than proposing yet another method, we revisit the problem from first principles and reassess its core challenges. We adop…
Asynchronous Denoising Diffusion Models for Aligning Text-to-Image Generation
Zijing Hu, Yunze Tong, Fengda Zhang +3
Diffusion models have achieved impressive results in generating high-quality images. Yet, they often struggle to faithfully align the generated images with the input prompts. This…
Follow-Your-Preference: Towards Preference-Aligned Image Inpainting
Yutao Shen, Junkun Yuan, Toru Aonishi +2
This paper investigates image inpainting with preference alignment. Instead of introducing a novel method, we go back to basics and revisit fundamental problems in achieving such a…
Follow-Your-Emoji-Faster: Towards Efficient, Fine-Controllable, and Expressive Freestyle Portrait Animation
Yue Ma, Zexuan Yan, Hongyu Liu +11
We present Follow-Your-Emoji-Faster, an efficient diffusion-based framework for freestyle portrait animation driven by facial landmarks. The main challenges in this task are preser…
Hunyuan-Game: Industrial-grade Intelligent Game Creation Model
Ruihuang Li, Caijin Zhou, Shoujian Zheng +55
Intelligent game creation represents a transformative advancement in game development, utilizing generative artificial intelligence to dynamically generate and enhance game content…
HunyuanVideo: A Systematic Framework For Large Video Generative Models
Weijie Kong, Qi Tian, Zijian Zhang +49
Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-sourc…
Follow-Your-Canvas: Higher-Resolution Video Outpainting with Extensive Content Generation
Qihua Chen, Yue Ma, Hongfa Wang +7
This paper explores higher-resolution video outpainting with extensive content generation. We point out common issues faced by existing methods when attempting to largely outpaint…
Follow-Your-Emoji: Fine-Controllable and Expressive Freestyle Portrait Animation
Yue Ma, Hongyu Liu, Hongfa Wang +8
We present Follow-Your-Emoji, a diffusion-based framework for portrait animation, which animates a reference portrait with target landmark sequences. The main challenge of portrait…
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
Junkun Yuan, Xinyu Zhang, Hao Zhou +12
Model pre-training is essential in human-centric perception. In this paper, we first introduce masked image modeling (MIM) as a pre-training approach for this task. Upon revisiting…
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization
Yunze Tong, Junkun Yuan, Min Zhang +4
Domain generalization (DG) is a prevalent problem in real-world applications, which aims to train well-generalized models for unseen target domains by utilizing several source doma…
Universal Domain Adaptation via Compressive Attention Matching
Didi Zhu, Yincuan Li, Junkun Yuan +3
Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set. The challenge lies in ho…
Task-Oriented Multi-Modal Mutual Leaning for Vision-Language Models
Sifan Long, Zhen Zhao, Junkun Yuan +5
Prompt learning has become one of the most efficient paradigms for adapting large pre-trained vision-language models to downstream tasks. Current state-of-the-art methods, like CoO…
CAE v2: Context Autoencoder with CLIP Target
Xinyu Zhang, Jiahui Chen, Junkun Yuan +10
Masked image modeling (MIM) learns visual representation by masking and reconstructing image patches. Applying the reconstruction supervision on the CLIP representation has been pr…
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization
Junkun Yuan, Xu Ma, Defang Chen +3
Considerable progress has been made in domain generalization (DG) which aims to learn a generalizable model from multiple well-annotated source domains to unknown target domains. H…
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation
Xu Ma, Junkun Yuan, Yen-wei Chen +2
Unsupervised domain adaptation (UDA) aims to learn transferable knowledge from a labeled source domain and adapts a trained model to an unlabeled target domain. To bridge the gap b…
Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization
Junkun Yuan, Xu Ma, Defang Chen +3
Domain generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. The existing DG methods usually exploit the…
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Junkun Yuan, Xu Ma, Ruoxuan Xiong +5
Domain generalization (DG) aims to learn from multiple source domains a model that can generalize well on unseen target domains. Existing DG methods mainly learn the representation…
Domain-Specific Bias Filtering for Single Labeled Domain Generalization
Junkun Yuan, Xu Ma, Defang Chen +3
Conventional Domain Generalization (CDG) utilizes multiple labeled source datasets to train a generalizable model for unseen target domains. However, due to expensive annotation co…
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
Junkun Yuan, Anpeng Wu, Kun Kuang +4
Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confo…
Learning Decomposed Representation for Counterfactual Inference
Anpeng Wu, Kun Kuang, Junkun Yuan +5
The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Most of the previous methods realized confounder balancin…
Subgraph Networks with Application to Structural Feature Space Expansion
Qi Xuan, Jinhuan Wang, Minghao Zhao +4
Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as mo…