30 citations · 113 across the 35 of their papers we have counts for
35 papers
MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Models
Yongshun Zhang, Zhongyi Fan, Yonghang Zhang +6
In recent years, large-scale generative models for visual content (\textit{e.g.,} images, videos, and 3D objects/scenes) have made remarkable progress. However, training large-scal…
RDPO: Real Data Preference Optimization for Physics Consistency Video Generation
Wenxu Qian, Chaoyue Wang, Hou Peng +3
Video generation techniques have achieved remarkable advancements in visual quality, yet faithfully reproducing real-world physics remains elusive. Preference-based model post-trai…
Semantix: An Energy Guided Sampler for Semantic Style Transfer
Huiang He, Minghui Hu, Chuanxia Zheng +2
Recent advances in style and appearance transfer are impressive, but most methods isolate global style and local appearance transfer, neglecting semantic correspondence. Additional…
MagicNaming: Consistent Identity Generation by Finding a "Name Space" in T2I Diffusion Models
Jing Zhao, Heliang Zheng, Chaoyue Wang +3
Large-scale text-to-image diffusion models, (e.g., DALL-E, SDXL) are capable of generating famous persons by simply referring to their names. Is it possible to make such models gen…
One-shot Generative Domain Adaptation in 3D GANs
Ziqiang Li, Yi Wu, Chaoyue Wang +2
3D-aware image generation necessitates extensive training data to ensure stable training and mitigate the risk of overfitting. This paper first considers a novel task known as One-…
Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment
Liyao Tang, Zhe Chen, Shanshan Zhao +2
Label-efficient segmentation aims to perform effective segmentation on input data using only sparse and limited ground-truth labels for training. This topic is widely studied in 3D…
Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm
Yi Wu, Ziqiang Li, Heliang Zheng +2
Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific i…
When ControlNet Meets Inexplicit Masks: A Case Study of ControlNet on its Contour-following Ability
Wenjie Xuan, Yufei Xu, Shanshan Zhao +4
ControlNet excels at creating content that closely matches precise contours in user-provided masks. However, when these masks contain noise, as a frequent occurrence with non-exper…
Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping
Jianbin Zheng, Minghui Hu, Zhongyi Fan +4
Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in ac…
One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls
Minghui Hu, Jianbin Zheng, Chuanxia Zheng +3
It is well known that many open-released foundational diffusion models have difficulty in generating images that substantially depart from average brightness, despite such images b…
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation
Ziqiang Li, Chaoyue Wang, Xue Rui +3
Few-shot image generation aims to train generative models using a small number of training images. When there are few images available for training (e.g. 10 images), Learning From…
HandRefiner: Refining Malformed Hands in Generated Images by Diffusion-based Conditional Inpainting
Wenquan Lu, Yufei Xu, Jing Zhang +2
Diffusion models have achieved remarkable success in generating realistic images but suffer from generating accurate human hands, such as incorrect finger counts or irregular shape…
Decompose Semantic Shifts for Composed Image Retrieval
Xingyu Yang, Daqing Liu, Heng Zhang +3
Composed image retrieval is a type of image retrieval task where the user provides a reference image as a starting point and specifies a text on how to shift from the starting poin…
Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image Generation
Zhuqiang Lu, Kun Hu, Chaoyue Wang +2
A 360-degree (omni-directional) image provides an all-encompassing spherical view of a scene. Recently, there has been an increasing interest in synthesising 360-degree images from…
Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation
Yiyang Chen, Shanshan Zhao, Changxing Ding +3
In recent years, cross-modal domain adaptation has been studied on the paired 2D image and 3D LiDAR data to ease the labeling costs for 3D LiDAR semantic segmentation (3DLSS) in th…
Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation
Minghui Hu, Jianbin Zheng, Daqing Liu +4
Text-conditional diffusion models are able to generate high-fidelity images with diverse contents. However, linguistic representations frequently exhibit ambiguous descriptions of…
MMoT: Mixture-of-Modality-Tokens Transformer for Composed Multimodal Conditional Image Synthesis
Jianbin Zheng, Daqing Liu, Chaoyue Wang +4
Existing multimodal conditional image synthesis (MCIS) methods generate images conditioned on any combinations of various modalities that require all of them must be exactly confor…
All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation
Liyao Tang, Zhe Chen, Shanshan Zhao +2
Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. Existing methods often rely on empiri…
ESceme: Vision-and-Language Navigation with Episodic Scene Memory
Qi Zheng, Daqing Liu, Chaoyue Wang +3
Vision-and-language navigation (VLN) simulates a visual agent that follows natural-language navigation instructions in real-world scenes. Existing approaches have made enormous pro…
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Chao Xue, Wei Liu, Shuai Xie +25
Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to t…
Eliminating Contextual Prior Bias for Semantic Image Editing via Dual-Cycle Diffusion
Zuopeng Yang, Tianshu Chu, Xin Lin +4
The recent success of text-to-image generation diffusion models has also revolutionized semantic image editing, enabling the manipulation of images based on query/target texts. Des…
Diff-Font: Diffusion Model for Robust One-Shot Font Generation
Haibin He, Xinyuan Chen, Chaoyue Wang +4
Font generation is a difficult and time-consuming task, especially in those languages using ideograms that have complicated structures with a large number of characters, such as Ch…
Unified Discrete Diffusion for Simultaneous Vision-Language Generation
Minghui Hu, Chuanxia Zheng, Heliang Zheng +5
The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In thi…
Cross-Modal Contrastive Learning for Robust Reasoning in VQA
Qi Zheng, Chaoyue Wang, Daqing Liu +2
Multi-modal reasoning in visual question answering (VQA) has witnessed rapid progress recently. However, most reasoning models heavily rely on shortcuts learned from training data,…