447 citations · 915 across the 26 of their papers we have counts for
23 papers · 1 filter
Sequential Point Clouds: A Survey
Haiyan Wang, Yingli Tian
Point cloud has drawn more and more research attention as well as real-world applications. However, many of these applications (e.g. autonomous driving and robotic manipulation) ar…
PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation
Haiyan Wang, Will Hutchcroft, Yuguang Li +4
In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360 panoramas. Our system, called Position-aware Stereo Merging Network or PSM…
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth
Ziyue Feng, Liang Yang, Longlong Jing +3
Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the misma…
The State of Aerial Surveillance: A Survey
Kien Nguyen, Clinton Fookes, Sridha Sridharan +4
The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and…
Multimodal Semi-Supervised Learning for 3D Objects
Zhimin Chen, Longlong Jing, Yang Liang +2
In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data. There is an emerging need to improve data efficiency for 3D task…
Deep Learning-based Action Detection in Untrimmed Videos: A Survey
Elahe Vahdani, Yingli Tian
Understanding human behavior and activity facilitates advancement of numerous real-world applications, and is critical for video analysis. Despite the progress of action recognitio…
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
Ziyue Feng, Longlong Jing, Peng Yin +2
Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfacto…
FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds
Haiyan Wang, Jiahao Pang, Muhammad A. Lodhi +2
Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is es…
Cross-modal Center Loss
Longlong Jing, Elahe Vahdani, Jiaxing Tan +1
Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the feature…
Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods
Yucheng Chen, Yingli Tian, Mingyi He
Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images…
Self-supervised Modal and View Invariant Feature Learning
Longlong Jing, Yucheng Chen, Ling Zhang +2
Most of the existing self-supervised feature learning methods for 3D data either learn 3D features from point cloud data or from multi-view images. By exploring the inherent multi-…
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes
Haiyan Wang, Xuejian Rong, Liang Yang +3
The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation, especially for scenes in the wild with varieties of different objects.…
Recognizing American Sign Language Nonmanual Signal Grammar Errors in Continuous Videos
Elahe Vahdani, Longlong Jing, Yingli Tian +1
As part of the development of an educational tool that can help students achieve fluency in American Sign Language (ASL) through independent and interactive practice with immediate…
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
Longlong Jing, Yucheng Chen, Ling Zhang +2
The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue,…
VideoSSL: Semi-Supervised Learning for Video Classification
Longlong Jing, Toufiq Parag, Zhe Wu +2
We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised…
Recognizing American Sign Language Manual Signs from RGB-D Videos
Longlong Jing, Elahe Vahdani, Matt Huenerfauth +1
In this paper, we propose a 3D Convolutional Neural Network (3DCNN) based multi-stream framework to recognize American Sign Language (ASL) manual signs (consisting of movements of…
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
Longlong Jing, Yingli Tian
Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vi…
LGAN: Lung Segmentation in CT Scans Using Generative Adversarial Network
Jiaxing Tan, Longlong Jing, Yumei Huo +2
Lung segmentation in computerized tomography (CT) images is an important procedure in various lung disease diagnosis. Most of the current lung segmentation approaches are performed…
Coarse-to-fine Semantic Segmentation from Image-level Labels
Longlong Jing, Yucheng Chen, Yingli Tian
Deep neural network-based semantic segmentation generally requires large-scale cost extensive annotations for training to obtain better performance. To avoid pixel-wise segmentatio…
Discovering Spatio-Temporal Action Tubes
Yuancheng Ye, Xiaodong Yang, Yingli Tian
In this paper, we address the challenging problem of spatial and temporal action detection in videos. We first develop an effective approach to localize frame-level action regions…
Incremental Scene Synthesis
Benjamin Planche, Xuejian Rong, Ziyan Wu +5
We present a method to incrementally generate complete 2D or 3D scenes with the following properties: (a) it is globally consistent at each step according to a learned scene prior,…
Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction
Longlong Jing, Xiaodong Yang, Jingen Liu +1
The success of deep neural networks generally requires a vast amount of training data to be labeled, which is expensive and unfeasible in scale, especially for video collections. T…
Self-Guiding Multimodal LSTM - when we do not have a perfect training dataset for image captioning
Yang Xian, Yingli Tian
In this paper, a self-guiding multimodal LSTM (sg-LSTM) image captioning model is proposed to handle uncontrolled imbalanced real-world image-sentence dataset. We collect FlickrNYC…