no code implementations • 12 Mar 2024 • Beibei Lin, Yeying Jin, Wending Yan, Wei Ye, Yuan Yuan, Robby T. Tan
By increasing the noise values to approach as high as the pixel intensity values of the glow and light effect blended images, our augmentation becomes severe, resulting in stronger priors.
no code implementations • 1 Jan 2024 • Beibei Lin, Yeying Jin, Wending Yan, Wei Ye, Yuan Yuan, Shunli Zhang, Robby Tan
However, the intricacies of the real world, particularly with the presence of light effects and low-light regions affected by noise, create significant domain gaps, hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and color shifts.
1 code implementation • 3 Aug 2023 • Yeying Jin, Beibei Lin, Wending Yan, Yuan Yuan, Wei Ye, Robby T. Tan
In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions.
no code implementations • ICCV 2023 • Ming Wang, Xianda Guo, Beibei Lin, Tian Yang, Zheng Zhu, Lincheng Li, Shunli Zhang, Xin Yu
This is the first framework on gait recognition that is designed to focus on the extraction of dynamic features.
no code implementations • 15 Nov 2022 • Beibei Lin, Chen Liu, Ming Wang, Lincheng Li, Shunli Zhang, Robby T. Tan, Xin Yu
Existing gait recognition frameworks retrieve an identity in the gallery based on the distance between a probe sample and the identities in the gallery.
2 code implementations • 2 Aug 2022 • Beibei Lin, Shunli Zhang, Ming Wang, Lincheng Li, Xin Yu
GFR extractor aims to extract contextual information, e. g., the relationship among various body parts, and the mask-based LFR extractor is presented to exploit the detailed posture changes of local regions.
no code implementations • ICCV 2021 • Xianda Guo, Zheng Zhu, Tian Yang, Beibei Lin, JunJie Huang, Jiankang Deng, Guan Huang, Jie zhou, Jiwen Lu
To the best of our knowledge, this is the first large-scale dataset for gait recognition in the wild.
no code implementations • 8 Mar 2022 • Chuanfu Shen, Beibei Lin, Shunli Zhang, George Q. Huang, Shiqi Yu, Xin Yu
Also, we design an Inception-like ReverseMask Block, which has three branches composed of a global branch, a feature dropping branch, and a feature scaling branch.
Ranked #2 on Gait Recognition on OUMVLP
1 code implementation • 8 Mar 2022 • Ming Wang, Beibei Lin, Xianda Guo, Lincheng Li, Zheng Zhu, Jiande Sun, Shunli Zhang, Xin Yu
ECM consists of the Spatial-Temporal feature extractor (ST), the Frame-Level feature extractor (FL) and SPB, and has two obvious advantages: First, each branch focuses on a specific representation, which can be used to improve the robustness of the network.
no code implementations • ICCV 2021 • Beibei Lin, Shunli Zhang, Xin Yu
Towards this goal, we take advantage of both global visual information and local region details and develop a Global and Local Feature Extractor (GLFE).