no code implementations • ECCV 2020 • Haoxian Zhang, Yang Zhao, Ronggang Wang
Inspired by classical pyramid energy minimization optical flow algorithms, this paper proposes a recurrent residual pyramid network (RRPN) for video frame interpolation.
1 code implementation • 24 Mar 2024 • Xiaoyun Zheng, Liwei Liao, Xufeng Li, Jianbo Jiao, Rongjie Wang, Feng Gao, Shiqi Wang, Ronggang Wang
To facilitate the development of these fields, in this paper, we present PKU-DyMVHumans, a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos.
no code implementations • 2 Feb 2024 • Jiayu Yang, Wei Jiang, Yongqi Zhai, Chunhui Yang, Ronggang Wang
This paper presents a learned video compression method in response to video compression track of the 6th Challenge on Learned Image Compression (CLIC), at DCC 2024. Specifically, we propose a unified contextual video compression framework (UCVC) for joint P-frame and B-frame coding.
no code implementations • 24 Jan 2024 • Pengcheng Zhao, Yanxiang Chen, Yang Zhao, Wei Jia, Zhao Zhang, Ronggang Wang, Richang Hong
Second, the natural co-occurrence of audio and video is utilized to learn the color semantic correlations between audio and visual scenes.
no code implementations • 16 Jan 2024 • Wei Jiang, Yongqi Zhai, Hangyu Li, Ronggang Wang
This short paper describes our method for the track of image compression.
1 code implementation • 28 Jul 2023 • Wei Jiang, Jiayu Yang, Yongqi Zhai, Feng Gao, Ronggang Wang
Additionally, to capture global contexts, we propose the linear complexity attention-based global correlations capturing by leveraging the decomposition of the softmax operation.
Ranked #1 on Image Compression on kodak
1 code implementation • 12 May 2023 • Peirong Ning, Wei Jiang, Ronggang Wang
In recent years, there has been rapid development in learned image compression techniques that prioritize ratedistortion-perceptual compression, preserving fine details even at lower bit-rates.
no code implementations • 19 Apr 2023 • Wei Jiang, Peirong Ning, Jiayu Yang, Yongqi Zhai, Feng Gao, Ronggang Wang
To tackle this issue, we propose Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression (LLIC).
no code implementations • 6 Mar 2023 • Feng Wang, Haihang Ruan, Fei Xiong, Jiayu Yang, Litian Li, Ronggang Wang
Using more reference frames can significantly improve the compression efficiency in neural video compression.
no code implementations • ICCV 2023 • Kaiqiang Xiong, Rui Peng, Zhe Zhang, Tianxing Feng, Jianbo Jiao, Feng Gao, Ronggang Wang
On the one hand, we present an image-level contrastive branch to guide the model to acquire more context awareness, thus leading to more complete depth estimation in indistinguishable regions.
no code implementations • CVPR 2023 • Ning Zhang, Yuyao Ye, Yang Zhao, Ronggang Wang
In this paper, we revisit the stack-based ITM approaches and propose a novel method to reconstruct HDR radiance from a single image, which only needs to estimate two exposure images.
1 code implementation • CVPR 2023 • Zhe Zhang, Rui Peng, Yuxi Hu, Ronggang Wang
To intensify the full-scene geometry perception of our model, we present the depth distribution similarity loss based on the Gaussian-Mixture Model assumption.
Ranked #2 on Point Clouds on Tanks and Temples
1 code implementation • 14 Nov 2022 • Wei Jiang, Jiayu Yang, Yongqi Zhai, Peirong Ning, Feng Gao, Ronggang Wang
Based on MEM and MEM$^+$, we propose image compression models MLIC and MLIC$^+$.
Ranked #1 on Image Compression on kodak
no code implementations • 31 Oct 2022 • Litian Li, Zheng Yang, Ronggang Wang
Benefit from flexible network designs and end-to-end joint optimization approach, learned image compression (LIC) has demonstrated excellent coding performance and practical feasibility in recent years.
1 code implementation • CVPR 2022 • Rui Peng, Rongjie Wang, Zhenyu Wang, Yawen Lai, Ronggang Wang
Depth estimation is solved as a regression or classification problem in existing learning-based multi-view stereo methods.
Ranked #9 on 3D Reconstruction on DTU
no code implementations • 4 Jan 2022 • Yi Ma, Yongqi Zhai, Ronggang Wang
In this paper, we propose the first learned fine-grained scalable image compression model (DeepFGS) to overcome the above two shortcomings.
no code implementations • 29 Sep 2021 • Yang Zhao, Yanbo Ma, Yuan Chen, Wei Jia, Ronggang Wang, Xiaoping Liu
Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality.
Ranked #1 on Video Deinterlacing on MSU Deinterlacer Benchmark
1 code implementation • ICCV 2021 • Rui Peng, Ronggang Wang, Yawen Lai, Luyang Tang, Yangang Cai
Self-supervised methods play an increasingly important role in monocular depth estimation due to their great potential and low annotation cost.
no code implementations • 26 Mar 2021 • Dewang Hou, Yang Zhao, Yuyao Ye, Jiayu Yang, Jian Zhang, Ronggang Wang
Scaling and lossy coding are widely used in video transmission and storage.
2 code implementations • 10 Mar 2021 • Chong Mou, Jian Zhang, Xiaopeng Fan, Hangfan Liu, Ronggang Wang
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance.
no code implementations • 27 Nov 2020 • Yang Zhao, Wei Jia, Ronggang Wang
Traditional deinterlacing approaches are mainly focused on early interlacing scanning systems and thus cannot handle the complex and complicated artifacts in real-world early interlaced videos.
no code implementations • 13 Jun 2017 • Jinzhuo Wang, Wenmin Wang, Ronggang Wang, Wen Gao
We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction.
no code implementations • 12 Mar 2017 • Yang Zhao, Ronggang Wang, Wei Jia, Jianchao Yang, Wenmin Wang, Wen Gao
The proposed method consists of a learning stage and a reconstructing stage.
no code implementations • NeurIPS 2016 • Jinzhuo Wang, Wenmin Wang, Xiongtao Chen, Ronggang Wang, Wen Gao
This paper instead explores contexts as early as possible and leverages their evolutions for action recognition.