activity
20192022
most citedA High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark

10 citations · 23 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2022★ 6 cited

HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction

Yunze Liu, Yun Liu, Che Jiang +7

We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction. HOI4D consists of 2.4M RGB-D egoce…

cs.CV2021★ 1 cited

Deep auxiliary learning for visual localization using colorization task

Mi Tian, Qiong Nie, Hao Shen +1

Visual localization is one of the most important components for robotics and autonomous driving. Recently, inspiring results have been shown with CNN-based methods which provide a…

cs.CV2021

Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces

Alexander Sagel, Julian Wörmann, Hao Shen

Distance-based dynamic texture recognition is an important research field in multimedia processing with applications ranging from retrieval to segmentation of video data. Based on…

cs.CV2019★ 10 cited

A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark

Yinglu Liu, Hailin Shi, Yue Si +3

Face parsing, which is to assign a semantic label to each pixel in face images, has recently attracted increasing interest due to its huge application potentials. Although many fac…

cs.CV2019★ 5 cited

Grand Challenge of 106-Point Facial Landmark Localization

Yinglu Liu, Hao Shen, Yue Si +18

Facial landmark localization is a very crucial step in numerous face related applications, such as face recognition, facial pose estimation, face image synthesis, etc. However, pre…

cs.CV2019★ 1 cited

A Generative Map for Image-based Camera Localization

Mingpan Guo, Stefan Matthes, Jiaojiao Ye +1

In image-based camera localization systems, information about the environment is usually stored in some representation, which can be referred to as a map. Conventionally, most maps…