Search Results for author: Yuchen Fan

Found 26 papers, 15 papers with code

MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction

no code implementations20 Feb 2024 Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang, Fuyang Zhang, Yuchen Fan, Vikas Chandra, Yasutaka Furukawa, Rakesh Ranjan

This paper presents a neural architecture MVDiffusion++ for 3D object reconstruction that synthesizes dense and high-resolution views of an object given one or a few images without camera poses.

3D Object Reconstruction 3D Reconstruction +2

Training Neural Networks on RAW and HDR Images for Restoration Tasks

no code implementations6 Dec 2023 Lei Luo, ALEXANDRE CHAPIRO, Xiaoyu Xiang, Yuchen Fan, Rakesh Ranjan, Rafal Mantiuk

Our results indicate that neural networks train significantly better on HDR and RAW images represented in display-encoded color spaces, which offer better perceptual uniformity than linear spaces.

Deblurring Denoising +2

MoVideo: Motion-Aware Video Generation with Diffusion Models

no code implementations19 Nov 2023 Jingyun Liang, Yuchen Fan, Kai Zhang, Radu Timofte, Luc van Gool, Rakesh Ranjan

While recent years have witnessed great progress on using diffusion models for video generation, most of them are simple extensions of image generation frameworks, which fail to explicitly consider one of the key differences between videos and images, i. e., motion.

Image Generation Image to Video Generation +1

NOVIS: A Case for End-to-End Near-Online Video Instance Segmentation

no code implementations29 Aug 2023 Tim Meinhardt, Matt Feiszli, Yuchen Fan, Laura Leal-Taixe, Rakesh Ranjan

Until recently, the Video Instance Segmentation (VIS) community operated under the common belief that offline methods are generally superior to a frame by frame online processing.

Ranked #5 on Video Instance Segmentation on YouTube-VIS validation (using extra training data)

Instance Segmentation Segmentation +2

EBSR: Enhanced Binary Neural Network for Image Super-Resolution

no code implementations22 Mar 2023 Renjie Wei, Shuwen Zhang, Zechun Liu, Meng Li, Yuchen Fan, Runsheng Wang, Ru Huang

While the performance of deep convolutional neural networks for image super-resolution (SR) has improved significantly, the rapid increase of memory and computation requirements hinders their deployment on resource-constrained devices.

Binarization Image Super-Resolution +1

Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

1 code implementation CVPR 2023 Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, Luc van Gool

The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration.

Image Deblurring Image Defocus Deblurring +1

TCBERT: A Technical Report for Chinese Topic Classification BERT

no code implementations21 Nov 2022 Ting Han, Kunhao Pan, Xinyu Chen, Dingjie Song, Yuchen Fan, Xinyu Gao, Ruyi Gan, Jiaxing Zhang

Bidirectional Encoder Representations from Transformers or BERT~\cite{devlin-etal-2019-bert} has been one of the base models for various NLP tasks due to its remarkable performance.

Classification Contrastive Learning +1

VRT: A Video Restoration Transformer

1 code implementation28 Jan 2022 Jingyun Liang, JieZhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, Luc van Gool

Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.

Deblurring Denoising +7

Image Super-Resolution With Non-Local Sparse Attention

1 code implementation CVPR 2021 Yiqun Mei, Yuchen Fan, Yuqian Zhou

NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation.

Image Super-Resolution Long-range modeling

Neural Sparse Representation for Image Restoration

1 code implementation NeurIPS 2020 Yuchen Fan, Jiahui Yu, Yiqun Mei, Yulun Zhang, Yun Fu, Ding Liu, Thomas S. Huang

Inspired by the robustness and efficiency of sparse representation in sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks.

Image Compression Image Denoising +2

Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

3 code implementations CVPR 2020 Yiqun Mei, Yuchen Fan, Yuqian Zhou, Lichao Huang, Thomas S. Huang, Humphrey Shi

By combining the new CS-NL prior with local and in-scale non-local priors in a powerful recurrent fusion cell, we can find more cross-scale feature correlations within a single low-resolution (LR) image.

Feature Correlation Image Super-Resolution

Pyramid Attention Networks for Image Restoration

2 code implementations28 Apr 2020 Yiqun Mei, Yuchen Fan, Yulun Zhang, Jiahui Yu, Yuqian Zhou, Ding Liu, Yun Fu, Thomas S. Huang, Humphrey Shi

Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales.

Demosaicking Image Denoising +1

Scale-wise Convolution for Image Restoration

1 code implementation19 Dec 2019 Yuchen Fan, Jiahui Yu, Ding Liu, Thomas S. Huang

In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance.

Data Augmentation Image Compression +3

SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection

1 code implementation25 Jun 2019 Xiaofan Zhang, Cong Hao, Haoming Lu, Jiachen Li, Yuhong Li, Yuchen Fan, Kyle Rupnow, JinJun Xiong, Thomas Huang, Honghui Shi, Wen-mei Hwu, Deming Chen

Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.

object-detection Object Detection

YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

no code implementations6 Sep 2018 Ning Xu, Linjie Yang, Yuchen Fan, Dingcheng Yue, Yuchen Liang, Jianchao Yang, Thomas Huang

End-to-end sequential learning to explore spatialtemporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.

Image Segmentation Object +6

YouTube-VOS: Sequence-to-Sequence Video Object Segmentation

4 code implementations ECCV 2018 Ning Xu, Linjie Yang, Yuchen Fan, Jianchao Yang, Dingcheng Yue, Yuchen Liang, Brian Price, Scott Cohen, Thomas Huang

End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.

Ranked #12 on Video Object Segmentation on YouTube-VOS 2018 (F-Measure (Unseen) metric)

Image Segmentation Object +7

Non-Local Recurrent Network for Image Restoration

1 code implementation NeurIPS 2018 Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, Thomas S. Huang

The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood.

Feature Correlation Image Denoising +2

Robust Video Super-Resolution With Learned Temporal Dynamics

no code implementations ICCV 2017 Ding Liu, Zhaowen Wang, Yuchen Fan, Xian-Ming Liu, Zhangyang Wang, Shiyu Chang, Thomas Huang

Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network that is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner.

Relation Video Super-Resolution

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