no code implementations • ECCV 2020 • Mingzhu Zhu, Zhang Gao, Junzhi Yu, Bingwei He, Jiantao Liu
Guided refinement is a popular procedure of various image post-processing applications.
1 code implementation • 27 May 2024 • Shaoan Wang, Zhanhua Xin, Yaoqing Hu, Dongyue Li, Mingzhu Zhu, Junzhi Yu
Leveraging the asynchronous nature of events, a derivable piece-wise B-spline to represent camera pose continuously is introduced, enabling calibration for intrinsic parameters, extrinsic parameters, and time offset, with analytical Jacobians provided.
1 code implementation • 20 Oct 2023 • Shaoan Wang, Mingzhu Zhu, Yaoqing Hu, Dongyue Li, Fusong Yuan, Junzhi Yu
Experimental results demonstrate that the CylinderTag is a highly promising visual marker for use on cylindrical-like surfaces, thus offering important guidance for future research on high-precision visual localization of cylinder-shaped objects.
no code implementations • 28 Jun 2023 • Lingyu Si, Hongwei Dong, Wenwen Qiang, Junzhi Yu, Wenlong Zhai, Changwen Zheng, Fanjiang Xu, Fuchun Sun
To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks.
1 code implementation • 23 Jul 2022 • Zhiheng Wu, Yue Lu, Xingyu Chen, Zhengxing Wu, Liwen Kang, Junzhi Yu
In this work, we propose a novel OWOD problem called Unknown-Classified Open World Object Detection (UC-OWOD).
no code implementations • 6 Jul 2021 • Shuaizheng Yan, Xingyu Chen, Zhengxing Wu, Min Tan, Junzhi Yu
Experimental results show that the proposed method can be used to perform high-quality restoration of unconstrained underwater images without supervision.
no code implementations • 4 Mar 2020 • Xingyu Chen, Yue Lu, Zhengxing Wu, Junzhi Yu, Li Wen
According to our analysis, five key discoveries are reported: 1) Domain quality has an ignorable effect on within-domain convolutional representation and detection accuracy; 2) low-quality domain leads to higher generalization ability in cross-domain detection; 3) low-quality domain can hardly be well learned in a domain-mixed learning process; 4) degrading recall efficiency, restoration cannot improve within-domain detection accuracy; 5) visual restoration is beneficial to detection in the wild by reducing the domain shift between training data and real-world scenes.
no code implementations • 22 Dec 2019 • Xingyu Chen, Zhengxing Wu, Junzhi Yu, Li Wen
From a robotic perspective, the importance of recall continuity and localization stability is equal to that of accuracy, but the AP is insufficient to reflect detectors' performance across time.
1 code implementation • 23 Jul 2018 • Xingyu Chen, Junzhi Yu, Shihan Kong, Zhengxing Wu, Li Wen
As for temporal detection in videos, temporal refinement networks (TRNet) and temporal dual refinement networks (TDRNet) are developed by propagating the refinement information across time.
1 code implementation • 1 Mar 2018 • Xingyu Chen, Junzhi Yu, Zhengxing Wu
Moreover, we develop a creative temporal analysis unit, namely, attentional ConvLSTM (AC-LSTM), in which a temporal attention mechanism is specially tailored for background suppression and scale suppression while a ConvLSTM integrates attention-aware features across time.
1 code implementation • 3 Dec 2017 • Xingyu Chen, Junzhi Yu, Shihan Kong, Zhengxing Wu, Xi Fang, Li Wen
More specifically, an underwater index is investigated to describe underwater properties, and a loss function based on the underwater index is designed to train the critic branch for underwater noise suppression.