52 citations · 381 across the 46 of their papers we have counts for
15 papers · 1 filter
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
Hao Tang, Dan Xu, Yan Yan +2
In this paper, we address the task of semantic-guided scene generation. One open challenge in scene generation is the difficulty of the generation of small objects and detailed loc…
Asymmetric GANs for Image-to-Image Translation
Hao Tang, Nicu Sebe
Existing models for unsupervised image translation with Generative Adversarial Networks (GANs) can learn the mapping from the source domain to the target domain using a cycle-consi…
Unified Generative Adversarial Networks for Controllable Image-to-Image Translation
Hao Tang, Hong Liu, Nicu Sebe
We propose a unified Generative Adversarial Network (GAN) for controllable image-to-image translation, i.e., transferring an image from a source to a target domain guided by contro…
AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks
Hao Tang, Hong Liu, Dan Xu +2
State-of-the-art methods in image-to-image translation are capable of learning a mapping from a source domain to a target domain with unpaired image data. Though the existing metho…
Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation
Hao Tang, Dan Xu, Gaowen Liu +3
In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (CGAN) for the task of keypoint-guided image generation. The proposed CGAN is a cross-modal f…
Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation
Bin Duan, Wei Wang, Hao Tang +2
Since we were babies, we intuitively develop the ability to correlate the input from different cognitive sensors such as vision, audio, and text. However, in machine learning, this…