Search Results for author: Xingyu Chen

Found 17 papers, 6 papers with code

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

no code implementations6 Jul 2021 Shuaizheng Yan, Xingyu Chen, Zhengxing Wu, Jian Wang, Yue Lu, Min Tan, Junzhi Yu

Our experimental results show that the proposed method is able to perform high-quality restoration for unconstrained underwater images without any supervision.

Underwater Image Restoration

Adaptive Feature Alignment for Adversarial Training

no code implementations31 May 2021 Tao Wang, Ruixin Zhang, Xingyu Chen, Kai Zhao, Xiaolin Huang, Yuge Huang, Shaoxin Li, Jilin Li, Feiyue Huang

Based on this observation, we propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths.

Adversarial Defense

Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration

1 code implementation CVPR 2021 Xingyu Chen, Yufeng Liu, Chongyang Ma, Jianlong Chang, Huayan Wang, Tian Chen, Xiaoyan Guo, Pengfei Wan, Wen Zheng

In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues.

WebSRC: A Dataset for Web-Based Structural Reading Comprehension

no code implementations23 Jan 2021 Lu Chen, Xingyu Chen, Zihan Zhao, Danyang Zhang, Jiabao Ji, Ao Luo, Yuxuan Xiong, Kai Yu

This task requires a system not only to understand the semantics of texts but also the structure of the web page.

Reading Comprehension

A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning

2 code implementations ECCV 2020 Xingyu Chen, Xuguang Lan, Fuchun Sun, Nanning Zheng

Using a gating mechanism that discriminates the unseen samples from the seen samples can decompose the GZSL problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised classification problem.

Generalized Zero-Shot Learning

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 Detection

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 Video Object Detection

Proportionally Fair Clustering

no code implementations9 May 2019 Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala

We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context.


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.

Real-Time Object Detection

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

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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