no code implementations • 29 Sep 2021 • Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan
Traditionally, the primary goal of LL is to achieve the trade-off between the Stability (remembering past tasks) and Plasticity (adapting to new tasks).
1 code implementation • 6 Aug 2021 • Ye Liu, Lei Zhu, Shunda Pei, Huazhu Fu, Jing Qin, Qing Zhang, Liang Wan, Wei Feng
Our DID-Net predicts the three component maps by progressively integrating features across scales, and refines each map by passing an independent refinement network.
Ranked #2 on
Image Dehazing
on Haze4k
1 code implementation • CVPR 2021 • Zhihao Chen, Liang Wan, Lei Zhu, Jia Shen, Huazhu Fu, Wennan Liu, Jing Qin
The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection.
no code implementations • 22 Feb 2020 • Qian Zhang, Wei Feng, Liang Wan, Fei-Peng Tian, Xiaowei Wang, Ping Tan
Besides, we also theoretically prove the invariance of our ALR approach to the ambiguity of normal and lighting decomposition.
no code implementations • 21 Aug 2019 • Qing Guo, Wei Feng, Zhihao Chen, Ruijun Gao, Liang Wan, Song Wang
In this paper, we address these two problems by constructing a Blurred Video Tracking benchmark, which contains a variety of videos with different levels of motion blurs, as well as ground truth tracking results for evaluating trackers.
no code implementations • 26 Jul 2019 • Ruize Han, Yujun Zhang, Wei Feng, Chenxing Gong, Xiao-Yu Zhang, Jiewen Zhao, Liang Wan, Song Wang
However, for such collaborative analysis, the first step is to associate people, referred to as subjects in this paper, across these two views.
1 code implementation • 5 Dec 2018 • Mengya Gao, Yujun Shen, Quanquan Li, Junjie Yan, Liang Wan, Dahua Lin, Chen Change Loy, Xiaoou Tang
Knowledge Distillation (KD) aims at improving the performance of a low-capacity student model by inheriting knowledge from a high-capacity teacher model.
no code implementations • ICCV 2017 • Qing Guo, Wei Feng, Ce Zhou, Rui Huang, Liang Wan, Song Wang
How to effectively learn temporal variation of target appearance, to exclude the interference of cluttered background, while maintaining real-time response, is an essential problem of visual object tracking.
Ranked #5 on
Visual Object Tracking
on OTB-2013
no code implementations • ICCV 2015 • Wei Feng, Fei-Peng Tian, Qian Zhang, Nan Zhang, Liang Wan, Jizhou Sun
To guarantee detection sensitivity and accuracy of minute changes, in an observation, we capture a group of images under multiple illuminations, which need only to be roughly aligned to the last time lighting conditions.
no code implementations • CVPR 2013 • Liang Li, Wei Feng, Liang Wan, Jiawan Zhang
For this purpose, we aim at constructing maximum cohesive SP-grid, which is composed of real nodes, i. e. SPs, and dummy nodes that are meaningless in the image with only position-taking function in the grid.