341 citations · 1.9k across the 24 of their papers we have counts for
9 papers · 1 filter
LinesToFacePhoto: Face Photo Generation from Lines with Conditional Self-Attention Generative Adversarial Network
Yuhang Li, Xuejin Chen, Feng Wu +1
In this paper, we explore the task of generating photo-realistic face images from lines. Previous methods based on conditional generative adversarial networks (cGANs) have shown th…
Context-Aware Visual Policy Network for Fine-Grained Image Captioning
Zheng-Jun Zha, Daqing Liu, Hanwang Zhang +2
With the maturity of visual detection techniques, we are more ambitious in describing visual content with open-vocabulary, fine-grained and free-form language, i.e., the task of im…
Camera Lens Super-Resolution
Chang Chen, Zhiwei Xiong, Xinmei Tian +2
Existing methods for single image super-resolution (SR) are typically evaluated with synthetic degradation models such as bicubic or Gaussian downsampling. In this paper, we invest…
Sequential Gating Ensemble Network for Noise Robust Multi-Scale Face Restoration
Zhibo Chen, Jianxin Lin, Tiankuang Zhou +1
Face restoration from low resolution and noise is important for applications of face analysis recognition. However, most existing face restoration models omit the multiple scale is…
Learning to Assemble Neural Module Tree Networks for Visual Grounding
Daqing Liu, Hanwang Zhang, Feng Wu +1
Visual grounding, a task to ground (i.e., localize) natural language in images, essentially requires composite visual reasoning. However, existing methods over-simplify the composi…
Context-Aware Visual Policy Network for Sequence-Level Image Captioning
Daqing Liu, Zheng-Jun Zha, Hanwang Zhang +2
Many vision-language tasks can be reduced to the problem of sequence prediction for natural language output. In particular, recent advances in image captioning use deep reinforceme…
A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels
Shaobo Min, Xuejin Chen, Zheng-Jun Zha +2
\begin{abstract} Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation. Although many semi-supervised methods have been propos…
Deeply Exploit Depth Information for Object Detection
Saihui Hou, Zilei Wang, Feng Wu
This paper addresses the issue on how to more effectively coordinate the depth with RGB aiming at boosting the performance of RGB-D object detection. Particularly, we investigate t…
Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation
Haitian Zheng, Yebin Liu, Mengqi Ji +2
This paper proposes a convolutional neural network that can fuse high-level prior for semantic image segmentation. Motivated by humans' vision recognition system, our key design is…