1.2k citations · 2.8k across the 67 of their papers we have counts for
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Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene
Suryansh Kumar, Yuchao Dai, Hongdong Li
This work addresses the task of dense 3D reconstruction of a complex dynamic scene from images. The prevailing idea to solve this task is composed of a sequence of steps and is dep…
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
Dongxu Li, Cristian Rodriguez Opazo, Xin Yu +1
Vision-based sign language recognition aims at helping deaf people to communicate with others. However, most existing sign language datasets are limited to a small number of words.…
Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention
Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Fatemeh Sadat Saleh +2
This paper studies the problem of temporal moment localization in a long untrimmed video using natural language as the query. Given an untrimmed video and a sentence as the query,…
Learning Trajectory Dependencies for Human Motion Prediction
Wei Mao, Miaomiao Liu, Mathieu Salzmann +1
Human motion prediction, i.e., forecasting future body poses given observed pose sequence, has typically been tackled with recurrent neural networks (RNNs). However, as evidenced b…
Optimal Feature Transport for Cross-View Image Geo-Localization
Yujiao Shi, Xin Yu, Liu Liu +2
This paper addresses the problem of cross-view image geo-localization, where the geographic location of a ground-level street-view query image is estimated by matching it against a…
Neural Collaborative Subspace Clustering
Tong Zhang, Pan Ji, Mehrtash Harandi +2
We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to pre…
Noise-Aware Unsupervised Deep Lidar-Stereo Fusion
Xuelian Cheng, Yiran Zhong, Yuchao Dai +2
In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth ma…
Deep Stacked Hierarchical Multi-patch Network for Image Deblurring
Hongguang Zhang, Yuchao Dai, Hongdong Li +1
Despite deep end-to-end learning methods have shown their superiority in removing non-uniform motion blur, there still exist major challenges with the current multi-scale and scale…
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes
Yiran Zhong, Pan Ji, Jianyuan Wang +2
Unsupervised deep learning for optical flow computation has achieved promising results. Most existing deep-net based methods rely on image brightness consistency and local smoothne…
Lending Orientation to Neural Networks for Cross-view Geo-localization
Liu Liu, Hongdong Li
This paper studies image-based geo-localization (IBL) problem using ground-to-aerial cross-view matching. The goal is to predict the spatial location of a ground-level query image…
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation
Suryansh Kumar, Ram Srivatsav Ghorakavi, Yuchao Dai +1
Recent geometric methods need reliable estimates of 3D motion parameters to procure accurate dense depth map of a complex dynamic scene from monocular images \cite{kumar2017monocul…
Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry
Dingfu Zhou, Yuchao Dai, Hongdong Li
Recovering the absolute metric scale from a monocular camera is a challenging but highly desirable problem for monocular camera-based systems. By using different kinds of cues, var…