512 citations · 1.2k across the 15 of their papers we have counts for
15 papers
Box-supervised Instance Segmentation with Level Set Evolution
Wentong Li, Wenyu Liu, Jianke Zhu +3
In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently att…
Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filters
Wenyu Liu, Wentong Li, Jianke Zhu +3
Semantic segmentation on driving-scene images is vital for autonomous driving. Although encouraging performance has been achieved on daytime images, the performance on nighttime im…
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
Wenyu Liu, Gaofeng Ren, Runsheng Yu +3
Though deep learning-based object detection methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality ima…
Boundary-preserving Mask R-CNN
Tianheng Cheng, Xinggang Wang, Lichao Huang +1
Tremendous efforts have been made to improve mask localization accuracy in instance segmentation. Modern instance segmentation methods relying on fully convolutional networks perfo…
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
Peng Tang, Xinggang Wang, Song Bai +4
Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a…
Object Detection in Videos by High Quality Object Linking
Peng Tang, Chunyu Wang, Xinggang Wang +3
Compared with object detection in static images, object detection in videos is more challenging due to degraded image qualities. An effective way to address this problem is to expl…
Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis
Pengyuan Lyu, Xiang Bai, Cong Yao +3
In this paper, we investigate the Chinese calligraphy synthesis problem: synthesizing Chinese calligraphy images with specified style from standard font(eg. Hei font) images (Fig.…
Point Linking Network for Object Detection
Xinggang Wang, Kaibing Chen, Zilong Huang +2
Object detection is a core problem in computer vision. With the development of deep ConvNets, the performance of object detectors has been dramatically improved. The deep ConvNets…
Deep Patch Learning for Weakly Supervised Object Classification and Discovery
Peng Tang, Xinggang Wang, Zilong Huang +2
Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background.…
Multiple Instance Detection Network with Online Instance Classifier Refinement
Peng Tang, Xinggang Wang, Xiang Bai +1
Of late, weakly supervised object detection is with great importance in object recognition. Based on deep learning, weakly supervised detectors have achieved many promising results…
TextBoxes: A Fast Text Detector with a Single Deep Neural Network
Minghui Liao, Baoguang Shi, Xiang Bai +2
This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward p…
Revisiting Multiple Instance Neural Networks
Xinggang Wang, Yongluan Yan, Peng Tang +2
Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great succ…
Deep FisherNet for Object Classification
Peng Tang, Xinggang Wang, Baoguang Shi +3
Despite the great success of convolutional neural networks (CNN) for the image classification task on datasets like Cifar and ImageNet, CNN's representation power is still somewhat…
Multi-Oriented Text Detection with Fully Convolutional Networks
Zheng Zhang, Chengquan Zhang, Wei Shen +3
In this paper, we propose a novel approach for text detec- tion in natural images. Both local and global cues are taken into account for localizing text lines in a coarse-to-fine p…
Deep Regression for Face Alignment
Baoguang Shi, Xiang Bai, Wenyu Liu +1
In this paper, we present a deep regression approach for face alignment. The deep architecture consists of a global layer and multi-stage local layers. We apply the back-propagatio…