no code implementations • 4 Jun 2023 • Shuo Ye, Yufeng Shi, Ruxin Wang, Yu Wang, Jiamiao Xu, Chuanwu Yang, Xinge You
Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC).
no code implementations • 26 Sep 2022 • Yufeng Shi, Xinge You, Jiamiao Xu, Feng Zheng, Qinmu Peng, Weihua Ou
Hashing that projects data into binary codes has shown extraordinary talents in cross-modal retrieval due to its low storage usage and high query speed.
no code implementations • ICCV 2023 • Maosheng Ye, Jiamiao Xu, Xunnong Xu, Tengfei Wang, Tongyi Cao, Qifeng Chen
Also, to model the multi-modality in motion forecasting, we design a novel self-ensembling scheme to obtain accurate teacher targets to enforce the self-constraints with multi-modality supervision.
Ranked #9 on Motion Forecasting on Argoverse CVPR 2020
no code implementations • 22 Mar 2020 • Jiamiao Xu, Fangzhao Wang, Qinmu Peng, Xinge You, Shuo Wang, Xiao-Yuan Jing, C. L. Philip Chen
Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution.
1 code implementation • 19 Nov 2018 • Peng Zhang, Shujian Yu, Jiamiao Xu, Xinge You, Xiubao Jiang, Xiao-Yuan Jing, DaCheng Tao
It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations.
no code implementations • 14 May 2018 • Xinge You, Jiamiao Xu, Wei Yuan, Xiao-Yuan Jing, DaCheng Tao, Taiping Zhang
Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision.
no code implementations • 19 Apr 2018 • Jiamiao Xu, Shujian Yu, Xinge You, Mengjun Leng, Xiao-Yuan Jing, C. L. Philip Chen
We present a novel cross-view classification algorithm where the gallery and probe data come from different views.