52 citations · 180 across the 16 of their papers we have counts for
16 papers
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…
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…
PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning
Mengya Han, Heliang Zheng, Chaoyue Wang +4
In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that lev…
Null-text Guidance in Diffusion Models is Secretly a Cartoon-style Creator
Jing Zhao, Heliang Zheng, Chaoyue Wang +3
Classifier-free guidance is an effective sampling technique in diffusion models that has been widely adopted. The main idea is to extrapolate the model in the direction of text gui…
Token Contrast for Weakly-Supervised Semantic Segmentation
Lixiang Ru, Heliang Zheng, Yibing Zhan +1
Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the local structure…
MagicFusion: Boosting Text-to-Image Generation Performance by Fusing Diffusion Models
Jing Zhao, Heliang Zheng, Chaoyue Wang +2
The advent of open-source AI communities has produced a cornucopia of powerful text-guided diffusion models that are trained on various datasets. While few explorations have been c…
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…
Domain Re-Modulation for Few-Shot Generative Domain Adaptation
Yi Wu, Ziqiang Li, Chaoyue Wang +4
In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only…
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…
3DDesigner: Towards Photorealistic 3D Object Generation and Editing with Text-guided Diffusion Models
Gang Li, Heliang Zheng, Chaoyue Wang +3
Text-guided diffusion models have shown superior performance in image/video generation and editing. While few explorations have been performed in 3D scenarios. In this paper, we di…
Leveraging GAN Priors for Few-Shot Part Segmentation
Mengya Han, Heliang Zheng, Chaoyue Wang +3
Few-shot part segmentation aims to separate different parts of an object given only a few annotated samples. Due to the challenge of limited data, existing works mainly focus on le…
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs
Ziqiang Li, Chaoyue Wang, Heliang Zheng +2
Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of training data, encounter several challenges for generating high-quality samples. Since…
SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
Gang Li, Heliang Zheng, Daqing Liu +3
Recently, significant progress has been made in masked image modeling to catch up to masked language modeling. However, unlike words in NLP, the lack of semantic decomposition of i…
Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment
Heliang Zheng, Huan Yang, Jianlong Fu +2
An important scenario for image quality assessment (IQA) is to evaluate image restoration (IR) algorithms. The state-of-the-art approaches adopt a full-reference paradigm that comp…
Learning Deep Bilinear Transformation for Fine-grained Image Representation
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha +1
Bilinear feature transformation has shown the state-of-the-art performance in learning fine-grained image representations. However, the computational cost to learn pairwise interac…
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha +1
Learning subtle yet discriminative features (e.g., beak and eyes for a bird) plays a significant role in fine-grained image recognition. Existing attention-based approaches localiz…