Search Results for author: Jinjian Wu

Found 7 papers, 6 papers with code

Generalizable No-Reference Image Quality Assessment via Deep Meta-learning

1 code implementation IEEE Transactions on Circuits and Systems for Video Technology 2021 Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi

Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions.

Meta-Learning No-Reference Image Quality Assessment

Searching Efficient Model-guided Deep Network for Image Denoising

no code implementations6 Apr 2021 Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, Leida Li, Guangming Shi

Similar to the success of NAS in high-level vision tasks, it is possible to find a memory and computationally efficient solution via NAS with highly competent denoising performance.

Image Denoising Neural Architecture Search

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

1 code implementation CVPR 2021 Tao Huang, Weisheng Dong, Xin Yuan, Jinjian Wu, Guangming Shi

Different from existing GSM models using hand-crafted scale priors (e. g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN).

Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment

1 code implementation ICCV 2021 Pengfei Chen, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi

From this adaptation, we split the data in target domain into confident and uncertain subdomains using the proposed uncertainty-based ranking function, through measuring their prediction confidences.

Unsupervised Domain Adaptation Video Quality Assessment +1

MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment

1 code implementation CVPR 2020 Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi

The underlying idea is to learn the meta-knowledge shared by human when evaluating the quality of images with various distortions, which can then be adapted to unknown distortions easily.

Meta-Learning No-Reference Image Quality Assessment

Feature-Fused SSD: Fast Detection for Small Objects

1 code implementation15 Sep 2017 Guimei Cao, Xuemei Xie, Wenzhe Yang, Quan Liao, Guangming Shi, Jinjian Wu

We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects.

Small Object Detection

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