29 citations · 89 across the 13 of their papers we have counts for
13 papers
LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detection
Yutong Xiao, Ran Ran, Jiwei Wei +4
The rapid advancement of generative technologies has made synthetic images nearly indistinguishable from real ones, thereby creating an urgent need for robust detectors to counter…
UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement
Yaofeng Xie, Lingwei Kong, Kai Chen +4
Learning-based underwater image enhancement (UIE) methods have made great progress. However, the lack of large-scale and high-quality paired training samples has become the main bo…
Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
Yan Kai, Liang Lanyue, Zheng Ziqiang +2
Underwater visual perception is essentially important for underwater exploration, archeology, ecosystem and so on. The low illumination, light reflections, scattering, absorption a…
ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval
Hao Ren, Ziqiang Zheng, Yang Wu +4
The huge domain gap between sketches and photos and the highly abstract sketch representations pose challenges for sketch-based image retrieval (\underline{SBIR}). The zero-shot sk…
ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation
Fan Yang, Xin Chang, Chenyu Dang +4
We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement. However, it remains challenging for refining MOTS results, which could be attri…
EncryptGAN: Image Steganography with Domain Transform
Ziqiang Zheng, Hongzhi Liu, Zhibin Yu +4
We propose an image steganographic algorithm called EncryptGAN, which disguises private image communication in an open communication channel. The insight is that content transform…
ReshapeGAN: Object Reshaping by Providing A Single Reference Image
Ziqiang Zheng, Yang Wu, Zhibin Yu +3
The aim of this work is learning to reshape the object in an input image to an arbitrary new shape, by just simply providing a single reference image with an object instance in the…
One-Shot Image-to-Image Translation via Part-Global Learning with a Multi-adversarial Framework
Ziqiang Zheng, Zhibin Yu, Haiyong Zheng +2
It is well known that humans can learn and recognize objects effectively from several limited image samples. However, learning from just a few images is still a tremendous challeng…
Generative Adversarial Network with Multi-Branch Discriminator for Cross-Species Image-to-Image Translation
Ziqiang Zheng, Zhibin Yu, Haiyong Zheng +3
Current approaches have made great progress on image-to-image translation tasks benefiting from the success of image synthesis methods especially generative adversarial networks (G…
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network
Ziqiang Zheng, Chao Wang, Zhibin Yu +2
Semantic layouts based Image synthesizing, which has benefited from the success of Generative Adversarial Network (GAN), has drawn much attention in these days. How to enhance the…
Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes
Ziqiang Zheng, Zhibin Yu, Haiyong Zheng +2
Generative Adversarial Networks are proved to be efficient on various kinds of image generation tasks. However, it is still a challenge if we want to generate images precisely. Man…
Unpaired Photo-to-Caricature Translation on Faces in the Wild
Ziqiang Zheng, Wang Chao, Zhibin Yu +3
Recently, image-to-image translation has been made much progress owing to the success of conditional Generative Adversarial Networks (cGANs). And some unpaired methods based on cyc…
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation
Chao Wang, Haiyong Zheng, Zhibin Yu +3
Image-to-image translation has been made much progress with embracing Generative Adversarial Networks (GANs). However, it's still very challenging for translation tasks that requir…