Search Results for author: Ren Yang

Found 23 papers, 18 papers with code

Detecting Reddit Users with Depression Using a Hybrid Neural Network

no code implementations3 Feb 2023 Ziyi Chen, Ren Yang, Sunyang Fu, Nansu Zong, Hongfang Liu, Ming Huang

In this work, we propose a hybrid deep learning model which combines a pretrained sentence BERT (SBERT) and convolutional neural network (CNN) to detect individuals with depression with their Reddit posts.

text-classification Text Classification

Advancing Learned Video Compression with In-loop Frame Prediction

1 code implementation13 Nov 2022 Ren Yang, Radu Timofte, Luc van Gool

In this paper, we propose an Advanced Learned Video Compression (ALVC) approach with the in-loop frame prediction module, which is able to effectively predict the target frame from the previously compressed frames, without consuming any bit-rate.


Implicit Neural Representations for Image Compression

no code implementations8 Dec 2021 Yannick Strümpler, Janis Postels, Ren Yang, Luc van Gool, Federico Tombari

Recently Implicit Neural Representations (INRs) gained attention as a novel and effective representation for various data types.

Image Compression Quantization

Perceptual Learned Video Compression with Recurrent Conditional GAN

3 code implementations7 Sep 2021 Ren Yang, Radu Timofte, Luc van Gool

This paper proposes a Perceptual Learned Video Compression (PLVC) approach with recurrent conditional GAN.

Video Compression

R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network

1 code implementation28 Jun 2021 Jiang Hai, Zhu Xuan, Songchen Han, Ren Yang, Yutong Hao, Fengzhu Zou, Fang Lin

Solving a series of degradation of low-light images can effectively improve the visual quality of images and the performance of high-level visual tasks.

Denoising Face Detection +1

Deep Homography for Efficient Stereo Image Compression

1 code implementation CVPR 2021 Xin Deng, Wenzhe Yang, Ren Yang, Mai Xu, Enpeng Liu, Qianhan Feng, Radu Timofte

To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i. e., H matrix.

Image Compression

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study

2 code implementations21 Apr 2021 Ren Yang, Radu Timofte

In our study, we analyze the proposed methods of the challenge and several methods in previous works on the proposed LDV dataset.

Video Enhancement

OpenDVC: An Open Source Implementation of the DVC Video Compression Method

4 code implementations29 Jun 2020 Ren Yang, Luc van Gool, Radu Timofte

At the time of writing this report, several learned video compression methods are superior to DVC, but currently none of them provides open source codes.


Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model

2 code implementations24 Jun 2020 Ren Yang, Fabian Mentzer, Luc van Gool, Radu Timofte

The experiments show that our approach achieves the state-of-the-art learned video compression performance in terms of both PSNR and MS-SSIM.


Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement

3 code implementations CVPR 2020 Ren Yang, Fabian Mentzer, Luc van Gool, Radu Timofte

In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively.

Image Compression MS-SSIM +2

Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution

1 code implementation ICCV 2019 Xin Deng, Ren Yang, Mai Xu, Pier Luigi Dragotti

In this paper, we propose a novel method based on wavelet domain style transfer (WDST), which achieves a better PD tradeoff than the GAN based methods.

Image Super-Resolution Style Transfer

Quality-Gated Convolutional LSTM for Enhancing Compressed Video

1 code implementation11 Mar 2019 Ren Yang, Xiaoyan Sun, Mai Xu, Wen-Jun Zeng

The past decade has witnessed great success in applying deep learning to enhance the quality of compressed video.

A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC

no code implementations5 Mar 2019 Tianyi Li, Mai Xu, Ren Yang, Xiaoming Tao

High efficiency video coding (HEVC) has brought outperforming efficiency for video compression.

Video Compression

MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video

1 code implementation26 Feb 2019 Qunliang Xing, Zhenyu Guan, Mai Xu, Ren Yang, Tie Liu, Zulin Wang

Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video.

Video Enhancement Video Restoration

Understanding and Predicting the Memorability of Outdoor Natural Scenes

2 code implementations9 Oct 2018 Jiaxin Lu, Mai Xu, Ren Yang, Zulin Wang

In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores.

What Makes Natural Scene Memorable?

no code implementations27 Aug 2018 Jiaxin Lu, Mai Xu, Ren Yang, Zulin Wang

Recent studies on image memorability have shed light on the visual features that make generic images, object images or face photographs memorable.

Multi-Frame Quality Enhancement for Compressed Video

1 code implementation CVPR 2018 Ren Yang, Mai Xu, Zulin Wang, Tianyi Li

In this paper, we investigate that heavy quality fluctuation exists across compressed video frames, and thus low quality frames can be enhanced using the neighboring high quality frames, seen as Multi-Frame Quality Enhancement (MFQE).

Motion Compensation Video Enhancement

Enhancing Quality for HEVC Compressed Videos

no code implementations20 Sep 2017 Ren Yang, Mai Xu, Tie Liu, Zulin Wang, Zhenyu Guan

Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P frames of HEVC videos.


Reducing Complexity of HEVC: A Deep Learning Approach

1 code implementation19 Sep 2017 Mai Xu, Tianyi Li, Zulin Wang, Xin Deng, Ren Yang, Zhenyu Guan

Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra- and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network.

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