109 citations · 223 across the 12 of their papers we have counts for
12 papers
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
Kunpeng Song, Yizhe Zhu, Bingchen Liu +3
In this paper, we present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. As foundational text-to-image models rapidly…
Shifted Diffusion for Text-to-image Generation
Yufan Zhou, Bingchen Liu, Yizhe Zhu +3
We present Corgi, a novel method for text-to-image generation. Corgi is based on our proposed shifted diffusion model, which achieves better image embedding generation from input t…
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song +1
Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the f…
Self-Supervised Sketch-to-Image Synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song +1
Imagining a colored realistic image from an arbitrarily drawn sketch is one of the human capabilities that we eager machines to mimic. Unlike previous methods that either requires…
TIME: Text and Image Mutual-Translation Adversarial Networks
Bingchen Liu, Kunpeng Song, Yizhe Zhu +2
Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I ge…
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Bingchen Liu, Yizhe Zhu, Zuohui Fu +2
Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regu…
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning
Yizhe Zhu, Jianwen Xie, Bingchen Liu +1
We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems. It is a generativ…
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Yizhe Zhu, Jianwen Xie, Zhiqiang Tang +2
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes. Existing approaches predominan…
CU-Net: Coupled U-Nets
Zhiqiang Tang, Xi Peng, Shijie Geng +2
We design a new connectivity pattern for the U-Net architecture. Given several stacked U-Nets, we couple each U-Net pair through the connections of their semantic blocks, resulting…
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu +2
Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks(GANs) to gene…
Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision
Mohamed Elhoseiny, Yizhe Zhu, Han Zhang +1
In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to c…
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
Yizhe Zhu, Ahmed Elgammal
The exponentially increasing use of moving platforms for video capture introduces the urgent need to develop the general background subtraction algorithms with the capability to de…