254 citations · 978 across the 26 of their papers we have counts for
15 papers · 1 filter
An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation
Jihan Yang, Ruijia Xu, Ruiyu Li +4
We focus on Unsupervised Domain Adaptation (UDA) for the task of semantic segmentation. Recently, adversarial alignment has been widely adopted to match the marginal distribution o…
Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
Li Jiang, Hengshuang Zhao, Shu Liu +3
We achieve 3D semantic scene labeling by exploring semantic relation between each point and its contextual neighbors through edges. Besides an encoder-decoder branch for predicting…
Reflective Decoding Network for Image Captioning
Lei Ke, Wenjie Pei, Ruiyu Li +2
State-of-the-art image captioning methods mostly focus on improving visual features, less attention has been paid to utilizing the inherent properties of language to boost captioni…
Non-local Recurrent Neural Memory for Supervised Sequence Modeling
Canmiao Fu, Wenjie Pei, Qiong Cao +4
Typical methods for supervised sequence modeling are built upon the recurrent neural networks to capture temporal dependencies. One potential limitation of these methods is that th…
Cross-Domain Adaptation for Animal Pose Estimation
Jinkun Cao, Hongyang Tang, Hao-Shu Fang +3
In this paper, we are interested in pose estimation of animals. Animals usually exhibit a wide range of variations on poses and there is no available animal pose dataset for traini…
Fast Point R-CNN
Yilun Chen, Shu Liu, Xiaoyong Shen +1
We present a unified, efficient and effective framework for point-cloud based 3D object detection. Our two-stage approach utilizes both voxel representation and raw point cloud dat…
SF-Net: Structured Feature Network for Continuous Sign Language Recognition
Zhaoyang Yang, Zhenmei Shi, Xiaoyong Shen +1
Continuous sign language recognition (SLR) aims to translate a signing sequence into a sentence. It is very challenging as sign language is rich in vocabulary, while many among the…
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
Zetong Yang, Yanan Sun, Shu Liu +2
We present a new two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD). The first stage is a bottom-up proposal generation network that uses raw p…
Landmark Assisted CycleGAN for Cartoon Face Generation
Ruizheng Wu, Xiaodong Gu, Xin Tao +3
In this paper, we are interested in generating an cartoon face of a person by using unpaired training data between real faces and cartoon ones. A major challenge of this task is th…
Attribute-Driven Spontaneous Motion in Unpaired Image Translation
Ruizheng Wu, Xin Tao, Xiaodong Gu +2
Current image translation methods, albeit effective to produce high-quality results in various applications, still do not consider much geometric transform. We in this paper propos…
2D Attentional Irregular Scene Text Recognizer
Pengyuan Lyu, Zhicheng Yang, Xinhang Leng +3
Irregular scene text, which has complex layout in 2D space, is challenging to most previous scene text recognizers. Recently, some irregular scene text recognizers either rectify t…
Region Refinement Network for Salient Object Detection
Zhuotao Tian, Hengshuang Zhao, Michelle Shu +4
Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection. In this paper, we propose a Region Refinement Network (RRN),…
Memory-Attended Recurrent Network for Video Captioning
Wenjie Pei, Jiyuan Zhang, Xiangrong Wang +3
Typical techniques for video captioning follow the encoder-decoder framework, which can only focus on one source video being processed. A potential disadvantage of such design is t…
Associatively Segmenting Instances and Semantics in Point Clouds
Xinlong Wang, Shu Liu, Xiaoyong Shen +2
A 3D point cloud describes the real scene precisely and intuitively.To date how to segment diversified elements in such an informative 3D scene is rarely discussed. In this paper,…
Human Pose Estimation with Spatial Contextual Information
Hong Zhang, Hao Ouyang, Shu Liu +4
We explore the importance of spatial contextual information in human pose estimation. Most state-of-the-art pose networks are trained in a multi-stage manner and produce several au…