Search Results for author: Junzhi Yu

Found 10 papers, 5 papers with code

CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects Pose Estimation Based on Projective Invariants

1 code implementation20 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.

Pose Estimation Visual Localization

A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning

no code implementations28 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.

Knowledge Distillation

UC-OWOD: Unknown-Classified Open World Object Detection

1 code implementation23 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).

Object object-detection +1

HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration

no code implementations6 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.

Underwater Image Restoration

Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes

no code implementations4 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.

Object object-detection +2

Rethinking Temporal Object Detection from Robotic Perspectives

no code implementations22 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.

Multi-Object Tracking Object +2

Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos

1 code implementation23 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.

Object object-detection +1

Temporally Identity-Aware SSD with Attentional LSTM

1 code implementation1 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.

object-detection Object Detection

Towards Real-Time Advancement of Underwater Visual Quality with GAN

1 code implementation3 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.

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