Search Results for author: Yulun Zhang

Found 110 papers, 75 papers with code

LatticeNet: Towards Lightweight Image Super-resolution with Lattice Block

2 code implementations ECCV 2020 Xiaotong Luo, Yuan Xie, Yulun Zhang, Yanyun Qu, Cuihua Li, Yun Fu

Drawing lessons from lattice filter bank, we design the lattice block (LB) in which two butterfly structures are applied to combine two RBs.

Image Super-Resolution

See More Details: Efficient Image Super-Resolution by Experts Mining

no code implementations5 Feb 2024 Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yulun Zhang, Radu Timofte

Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts.

Image Super-Resolution

Image Fusion via Vision-Language Model

no code implementations3 Feb 2024 Zixiang Zhao, Lilun Deng, Haowen Bai, Yukun Cui, Zhipeng Zhang, Yulun Zhang, Haotong Qin, Dongdong Chen, Jiangshe Zhang, Peng Wang, Luc van Gool

Therefore, we introduce a novel fusion paradigm named image Fusion via vIsion-Language Model (FILM), for the first time, utilizing explicit textual information in different source images to guide image fusion.

Language Modelling

Guidance Graph Optimization for Lifelong Multi-Agent Path Finding

no code implementations2 Feb 2024 Yulun Zhang, He Jiang, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li

Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in four benchmark maps, and (2) our update model can generate guidance graphs for as large as $93 \times 91$ maps and as many as 3000 agents.

Multi-Agent Path Finding

Scalable Mechanism Design for Multi-Agent Path Finding

no code implementations30 Jan 2024 Paul Friedrich, Yulun Zhang, Michael Curry, Ludwig Dierks, Stephen Mcaleer, Jiaoyang Li, Tuomas Sandholm, Sven Seuken

In this work, we introduce the problem of scalable mechanism design for MAPF and propose three strategyproof mechanisms, two of which even use approximate MAPF algorithms.

Multi-Agent Path Finding

DPoser: Diffusion Model as Robust 3D Human Pose Prior

1 code implementation9 Dec 2023 Junzhe Lu, Jing Lin, Hongkun Dou, Yulun Zhang, Yue Deng, Haoqian Wang

Modeling human pose is a cornerstone in applications from human-robot interaction to augmented reality, yet crafting a robust human pose prior remains a challenge due to biomechanical constraints and diverse human movements.

Denoising Human Mesh Recovery +1

MuRF: Multi-Baseline Radiance Fields

1 code implementation7 Dec 2023 Haofei Xu, Anpei Chen, Yuedong Chen, Christos Sakaridis, Yulun Zhang, Marc Pollefeys, Andreas Geiger, Fisher Yu

We present Multi-Baseline Radiance Fields (MuRF), a general feed-forward approach to solving sparse view synthesis under multiple different baseline settings (small and large baselines, and different number of input views).

Zero-shot Generalization

Binarized 3D Whole-body Human Mesh Recovery

1 code implementation24 Nov 2023 Zhiteng Li, Yulun Zhang, Jing Lin, Haotong Qin, Jinjin Gu, Xin Yuan, Linghe Kong, Xiaokang Yang

In this work, we propose a Binarized Dual Residual Network (BiDRN), a novel quantization method to estimate the 3D human body, face, and hands parameters efficiently.

Binarization Human Mesh Recovery +1

Image Super-Resolution with Text Prompt Diffusion

1 code implementation24 Nov 2023 Zheng Chen, Yulun Zhang, Jinjin Gu, Xin Yuan, Linghe Kong, Guihai Chen, Xiaokang Yang

Inspired by advancements in multi-modal methods and text prompt image processing, we introduce text prompts to image SR to provide degradation priors.

Image Generation Image Super-Resolution +1

Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model

1 code implementation20 Nov 2023 Chunming He, Chengyu Fang, Yulun Zhang, Kai Li, Longxiang Tang, Chenyu You, Fengyang Xiao, Zhenhua Guo, Xiu Li

These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components.

Image Restoration

Deep Equilibrium Diffusion Restoration with Parallel Sampling

1 code implementation20 Nov 2023 JieZhang Cao, Yue Shi, Kai Zhang, Yulun Zhang, Radu Timofte, Luc van Gool

Due to the inherent property of diffusion models, most of these methods need long serial sampling chains to restore HQ images step-by-step.

Image Restoration

Arbitrarily Scalable Environment Generators via Neural Cellular Automata

1 code implementation NeurIPS 2023 Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li

We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2, 350 robots.

OHQ: On-chip Hardware-aware Quantization

no code implementations5 Sep 2023 Wei Huang, Haotong Qin, Yangdong Liu, Jingzhuo Liang, Yulun Zhang, Ying Li, Xianglong Liu

Mixed-precision quantization leverages multiple bit-width architectures to unleash the accuracy and efficiency potential of quantized models.


Neural Gradient Regularizer

1 code implementation31 Aug 2023 Shuang Xu, Yifan Wang, Zixiang Zhao, Jiangjun Peng, Xiangyong Cao, Deyu Meng, Yulun Zhang, Radu Timofte, Luc van Gool

NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer.

Zero-Shot Learning

DiffI2I: Efficient Diffusion Model for Image-to-Image Translation

no code implementations26 Aug 2023 Bin Xia, Yulun Zhang, Shiyin Wang, Yitong Wang, Xinglong Wu, Yapeng Tian, Wenming Yang, Radu Timotfe, Luc van Gool

Compared to traditional DMs, the compact IPR enables DiffI2I to obtain more accurate outcomes and employ a lighter denoising network and fewer iterations.

Denoising Image-to-Image Translation +2

Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing

1 code implementation14 Aug 2023 Hao Shen, Zhong-Qiu Zhao, Yulun Zhang, Zhao Zhang

Multi-stage architectures have exhibited efficacy in image dehazing, which usually decomposes a challenging task into multiple more tractable sub-tasks and progressively estimates latent hazy-free images.

Image Dehazing

Recurrent Self-Supervised Video Denoising with Denser Receptive Field

no code implementations7 Aug 2023 Zichun Wang, Yulun Zhang, Debing Zhang, Ying Fu

However, under their blind spot constraints, previous self-supervised video denoising methods suffer from significant information loss and texture destruction in either the whole reference frame or neighbor frames, due to their inadequate consideration of the receptive field.

Denoising Video Denoising

Dual Aggregation Transformer for Image Super-Resolution

1 code implementation ICCV 2023 Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, Fisher Yu

Based on the above idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT), for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner.

Image Super-Resolution

Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects

no code implementations6 Aug 2023 Chunming He, Kai Li, Yachao Zhang, Yulun Zhang, Zhenhua Guo, Xiu Li, Martin Danelljan, Fisher Yu

On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect.

object-detection Object Detection

Consistency Regularization for Generalizable Source-free Domain Adaptation

no code implementations3 Aug 2023 Longxiang Tang, Kai Li, Chunming He, Yulun Zhang, Xiu Li

In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets.

Pseudo Label Source-Free Domain Adaptation

HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance

no code implementations15 Jul 2023 Chunming He, Kai Li, Guoxia Xu, Jiangpeng Yan, Longxiang Tang, Yulun Zhang, Xiu Li, YaoWei Wang

Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module.

Image Enhancement Medical Image Enhancement

Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging

no code implementations1 Jun 2023 Jiamian Wang, Zongliang Wu, Yulun Zhang, Xin Yuan, Tao Lin, Zhiqiang Tao

In this work, we tackle this challenge by marrying prompt tuning with FL to snapshot compressive imaging for the first time and propose an federated hardware-prompt learning (FedHP) method.

Federated Learning

Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution

no code implementations29 May 2023 Ruofan Zhang, Jinjin Gu, Haoyu Chen, Chao Dong, Yulun Zhang, Wenming Yang

In this work, we introduce a novel approach to craft training degradation distributions using a small set of reference images.


Alignment-free HDR Deghosting with Semantics Consistent Transformer

no code implementations ICCV 2023 Steven Tel, Zongwei Wu, Yulun Zhang, Barthélémy Heyrman, Cédric Demonceaux, Radu Timofte, Dominique Ginhac

The spatial attention aims to deal with the intra-image correlation to model the dynamic motion, while the channel attention enables the inter-image intertwining to enhance the semantic consistency across frames.

Image Generation

Multi-Modal Mutual Attention and Iterative Interaction for Referring Image Segmentation

no code implementations24 May 2023 Chang Liu, Henghui Ding, Yulun Zhang, Xudong Jiang

However, the generic attention mechanism in Transformer only uses the language input for attention weight calculation, which does not explicitly fuse language features in its output.

Image Segmentation Semantic Segmentation

Equivariant Multi-Modality Image Fusion

2 code implementations19 May 2023 Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Kai Zhang, Shuang Xu, Dongdong Chen, Radu Timofte, Luc van Gool

Multi-modality image fusion is a technique used to combine information from different sensors or modalities, allowing the fused image to retain complementary features from each modality, such as functional highlights and texture details.

Self-Supervised Learning

Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping

no code implementations NeurIPS 2023 Chunming He, Kai Li, Yachao Zhang, Guoxia Xu, Longxiang Tang, Yulun Zhang, Zhenhua Guo, Xiu Li

It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only provide weak supervision for model learning.

Segmentation Semantic Segmentation

Binarized Spectral Compressive Imaging

2 code implementations NeurIPS 2023 Yuanhao Cai, Yuxin Zheng, Jing Lin, Xin Yuan, Yulun Zhang, Haoqian Wang

Finally, our BiSRNet is derived by using the proposed techniques to binarize the base model.


Multi-Robot Coordination and Layout Design for Automated Warehousing

1 code implementation10 May 2023 Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li

We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability.

Layout Design Multi-Agent Path Finding

Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising

1 code implementation CVPR 2023 Miaoyu Li, Ji Liu, Ying Fu, Yulun Zhang, Dejing Dou

In this paper, we address these issues by proposing a spectral enhanced rectangle Transformer, driving it to explore the non-local spatial similarity and global spectral low-rank property of HSIs.

Hyperspectral Image Denoising Image Denoising

LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising

1 code implementation CVPR 2023 Zichun Wang, Ying Fu, Ji Liu, Yulun Zhang

Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blind-spot networks (BSNs).


Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution

2 code implementations ICCV 2023 Jiamian Wang, Huan Wang, Yulun Zhang, Yun Fu, Zhiqiang Tao

Second, existing pruning methods generally operate upon a pre-trained network for the sparse structure determination, hard to get rid of dense model training in the traditional SR paradigm.

Image Super-Resolution Network Pruning

DiffIR: Efficient Diffusion Model for Image Restoration

1 code implementation ICCV 2023 Bin Xia, Yulun Zhang, Shiyin Wang, Yitong Wang, Xinglong Wu, Yapeng Tian, Wenming Yang, Luc van Gool

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis process into a sequential application of a denoising network.

Denoising Image Generation +1

DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion

2 code implementations ICCV 2023 Zixiang Zhao, Haowen Bai, Yuanzhi Zhu, Jiangshe Zhang, Shuang Xu, Yulun Zhang, Kai Zhang, Deyu Meng, Radu Timofte, Luc van Gool

To leverage strong generative priors and address challenges such as unstable training and lack of interpretability for GAN-based generative methods, we propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM).


Recursive Generalization Transformer for Image Super-Resolution

1 code implementation11 Mar 2023 Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang

In this work, we propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images.

Image Reconstruction Image Super-Resolution

Xformer: Hybrid X-Shaped Transformer for Image Denoising

1 code implementation11 Mar 2023 Jiale Zhang, Yulun Zhang, Jinjin Gu, Jiahua Dong, Linghe Kong, Xiaokang Yang

The channel-wise Transformer block performs direct global context interactions across tokens defined by channel dimension.

Image Denoising

pyribs: A Bare-Bones Python Library for Quality Diversity Optimization

1 code implementation1 Mar 2023 Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Yulun Zhang, Nivedit Reddy Balam, Nathaniel Dennler, Sujay S. Garlanka, Nikitas Dimitri Klapsis, Stefanos Nikolaidis

Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem.

No One Left Behind: Real-World Federated Class-Incremental Learning

2 code implementations2 Feb 2023 Jiahua Dong, Hongliu Li, Yang Cong, Gan Sun, Yulun Zhang, Luc van Gool

These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories.

Class Incremental Learning Federated Learning +1

Camouflaged Object Detection With Feature Decomposition and Edge Reconstruction

no code implementations CVPR 2023 Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yulun Zhang, Zhenhua Guo, Xiu Li

COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries.

object-detection Object Detection

Degradation-Resistant Unfolding Network for Heterogeneous Image Fusion

no code implementations ICCV 2023 Chunming He, Kai Li, Guoxia Xu, Yulun Zhang, Runze Hu, Zhenhua Guo, Xiu Li

Heterogeneous image fusion (HIF) techniques aim to enhance image quality by merging complementary information from images captured by different sensors.

CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion

2 code implementations CVPR 2023 Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Shuang Xu, Zudi Lin, Radu Timofte, Luc van Gool

We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information.

object-detection Object Detection +1

Cross Aggregation Transformer for Image Restoration

3 code implementations24 Nov 2022 Zheng Chen, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan

The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows.

Image Restoration Inductive Bias

Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images

no code implementations9 Oct 2022 Jinjin Gu, Haoming Cai, Chenyu Dong, Ruofan Zhang, Yulun Zhang, Wenming Yang, Chun Yuan

We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image.

Quantization Super-Resolution

Accurate Image Restoration with Attention Retractable Transformer

1 code implementation4 Oct 2022 Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan

This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions.

Denoising Image Restoration +2

Basic Binary Convolution Unit for Binarized Image Restoration Network

2 code implementations2 Oct 2022 Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, Luc van Gool

In this study, we reconsider components in binary convolution, such as residual connection, BatchNorm, activation function, and structure, for IR tasks.

Binarization Image Restoration +1

S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction

1 code implementation24 Sep 2022 Jiamian Wang, Kunpeng Li, Yulun Zhang, Xin Yuan, Zhiqiang Tao

By observing this physical encoding procedure, two major challenges stand in the way of a high-fidelity reconstruction.

Blocking Image Reconstruction

Towards Interpretable Video Super-Resolution via Alternating Optimization

1 code implementation21 Jul 2022 JieZhang Cao, Jingyun Liang, Kai Zhang, Wenguan Wang, Qin Wang, Yulun Zhang, Hao Tang, Luc van Gool

These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences.

Deblurring Space-time Video Super-resolution +2

Structured Sparsity Learning for Efficient Video Super-Resolution

1 code implementation CVPR 2023 Bin Xia, Jingwen He, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Luc van Gool

In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks.

Video Super-Resolution

Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging

1 code implementation20 May 2022 Yuanhao Cai, Jing Lin, Haoqian Wang, Xin Yuan, Henghui Ding, Yulun Zhang, Radu Timofte, Luc van Gool

In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a compressed measurement.

Compressive Sensing Image Reconstruction +1

MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction

3 code implementations17 Apr 2022 Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Radu Timofte, Luc van Gool

Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI).

Spectral Reconstruction Spectral Super-Resolution

Cross-Modality High-Frequency Transformer for MR Image Super-Resolution

no code implementations29 Mar 2022 Chaowei Fang, Dingwen Zhang, Liang Wang, Yulun Zhang, Lechao Cheng, Junwei Han

Improving the resolution of magnetic resonance (MR) image data is critical to computer-aided diagnosis and brain function analysis.

Image Super-Resolution Vocal Bursts Intensity Prediction

Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

2 code implementations24 Mar 2022 Kai Zhang, Yawei Li, Jingyun Liang, JieZhang Cao, Yulun Zhang, Hao Tang, Deng-Ping Fan, Radu Timofte, Luc van Gool

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.

Image Denoising Image-to-Image Translation

Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution

1 code implementation16 Mar 2022 Bin Sun, Yulun Zhang, Songyao Jiang, Yun Fu

In this paper, we propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing an efficient and effective downsampling module into the SR task.

Image Super-Resolution

Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction

1 code implementation9 Mar 2022 Yuanhao Cai, Jing Lin, Xiaowan Hu, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, Luc van Gool

Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i. e., recovering the 3D hyperspectral images (HSIs) from a 2D compressive measurement.

Compressive Sensing Image Reconstruction +1

Texture-Based Error Analysis for Image Super-Resolution

no code implementations CVPR 2022 Salma Abdel Magid, Zudi Lin, Donglai Wei, Yulun Zhang, Jinjin Gu, Hanspeter Pfister

Our key contribution is to leverage a texture classifier, which enables us to assign patches with semantic labels, to identify the source of SR errors both globally and locally.

Image Super-Resolution SSIM

Modeling Mask Uncertainty in Hyperspectral Image Reconstruction

1 code implementation31 Dec 2021 Jiamian Wang, Yulun Zhang, Xin Yuan, Ziyi Meng, Zhiqiang Tao

Recently, hyperspectral imaging (HSI) has attracted increasing research attention, especially for the ones based on a coded aperture snapshot spectral imaging (CASSI) system.

Bilevel Optimization Image Reconstruction

Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding

1 code implementation7 Dec 2021 Yulun Zhang, Matthew C. Fontaine, Amy K. Hoover, Stefanos Nikolaidis

In a Hearthstone deckbuilding case study, we show that our approach improves the sample efficiency of MAP-Elites and outperforms a model trained offline with random decks, as well as a linear surrogate model baseline, setting a new state-of-the-art for quality diversity approaches in automated Hearthstone deckbuilding.

Aligned Structured Sparsity Learning for Efficient Image Super-Resolution

1 code implementation NeurIPS 2021 Yulun Zhang, Huan Wang, Can Qin, Yun Fu

To address the above issues, we propose aligned structured sparsity learning (ASSL), which introduces a weight normalization layer and applies $L_2$ regularization to the scale parameters for sparsity.

Image Super-Resolution Knowledge Distillation +3

Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

1 code implementation NeurIPS 2021 Can Qin, Handong Zhao, Lichen Wang, Huan Wang, Yulun Zhang, Yun Fu

For slow learning of graph similarity, this paper proposes a novel early-fusion approach by designing a co-attention-based feature fusion network on multilevel GNN features.

Anomaly Detection Graph Similarity +3

A Simple Approach to Continual Learning by Transferring Skill Parameters

no code implementations19 Oct 2021 K. R. Zentner, Ryan Julian, Ujjwal Puri, Yulun Zhang, Gaurav S. Sukhatme

We take a fresh look at this problem, by considering a setting in which the robot is limited to storing that knowledge and experience only in the form of learned skill policies.

Continual Learning

MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning

no code implementations29 Sep 2021 Yi Xu, Lichen Wang, Yizhou Wang, Can Qin, Yulun Zhang, Yun Fu

In this paper, we propose a novel framework, MemREIN, which considers Memorized, Restitution, and Instance Normalization for cross-domain few-shot learning.

Contrastive Learning cross-domain few-shot learning


no code implementations29 Sep 2021 Jiamian Wang, Yulun Zhang, Xin Yuan, Yun Fu, Zhiqiang Tao

As the inverse process of snapshot compressive imaging, the hyperspectral image (HSI) reconstruction takes the 2D measurement as input and posteriorly retrieves the captured 3D spatial-spectral signal.

Computational Efficiency Image Reconstruction

A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging

1 code implementation17 Aug 2021 Jiamian Wang, Yulun Zhang, Xin Yuan, Yun Fu, Zhiqiang Tao

The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way.

Compressive Sensing Computational Efficiency +4

Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning

no code implementations24 Jun 2021 K. R. Zentner, Ryan Julian, Ujjwal Puri, Yulun Zhang, Gaurav Sukhatme

We explore possible methods for multi-task transfer learning which seek to exploit the shared physical structure of robotics tasks.

Transfer Learning

Rethinking Adam: A Twofold Exponential Moving Average Approach

no code implementations22 Jun 2021 Yizhou Wang, Yue Kang, Can Qin, Huan Wang, Yi Xu, Yulun Zhang, Yun Fu

The intuition is that gradient with momentum contains more accurate directional information and therefore its second moment estimation is a more favorable option for learning rate scaling than that of the raw gradient.

On the Importance of Environments in Human-Robot Coordination

1 code implementation21 Jun 2021 Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang, Bryon Tjanaka, Stefanos Nikolaidis

When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks.

Pseudo 3D Auto-Correlation Network for Real Image Denoising

no code implementations CVPR 2021 Xiaowan Hu, Ruijun Ma, Zhihong Liu, Yuanhao Cai, Xiaole Zhao, Yulun Zhang, Haoqian Wang

The extraction of auto-correlation in images has shown great potential in deep learning networks, such as the self-attention mechanism in the channel domain and the self-similarity mechanism in the spatial domain.

Image Denoising

MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network

no code implementations CVPR 2021 Yulun Zhang, Kai Li, Kunpeng Li, Yun Fu

They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors.

Image Super-Resolution

ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation

1 code implementation ICCV 2021 Kai Li, Chang Liu, Handong Zhao, Yulun Zhang, Yun Fu

This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain.

Data Augmentation Domain Adaptation +1

Recent Advances on Neural Network Pruning at Initialization

2 code implementations11 Mar 2021 Huan Wang, Can Qin, Yue Bai, Yulun Zhang, Yun Fu

Neural network pruning typically removes connections or neurons from a pretrained converged model; while a new pruning paradigm, pruning at initialization (PaI), attempts to prune a randomly initialized network.

Benchmarking Network Pruning

GAN Inversion: A Survey

1 code implementation14 Jan 2021 Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, Ming-Hsuan Yang

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generator.

Image Manipulation Image Restoration

Context Reasoning Attention Network for Image Super-Resolution

no code implementations ICCV 2021 Yulun Zhang, Donglai Wei, Can Qin, Huan Wang, Hanspeter Pfister, Yun Fu

However, the basic convolutional layer in CNNs is designed to extract local patterns, lacking the ability to model global context.

Image Super-Resolution

Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution

no code implementations ICCV 2021 Salma Abdel Magid, Yulun Zhang, Donglai Wei, Won-Dong Jang, Zudi Lin, Yun Fu, Hanspeter Pfister

Specifically, we propose a dynamic high-pass filtering (HPF) module that locally applies adaptive filter weights for each spatial location and channel group to preserve high-frequency signals.

Image Super-Resolution

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

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

Adversarial Feature Hallucination Networks for Few-Shot Learning

1 code implementation CVPR 2020 Kai Li, Yulun Zhang, Kunpeng Li, Yun Fu

The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data.

Data Augmentation Few-Shot Learning +1

Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

3 code implementations CVPR 2020 Xiaoyu Xiang, Yapeng Tian, Yulun Zhang, Yun Fu, Jan P. Allebach, Chenliang Xu

Rather than synthesizing missing LR video frames as VFI networks do, we firstly temporally interpolate LR frame features in missing LR video frames capturing local temporal contexts by the proposed feature temporal interpolation network.

Space-time Video Super-resolution Video Frame Interpolation +1

Texture Hallucination for Large-Factor Painting Super-Resolution

no code implementations ECCV 2020 Yulun Zhang, Zhifei Zhang, Stephen DiVerdi, Zhaowen Wang, Jose Echevarria, Yun Fu

We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e. g., 8X, 16X).

Hallucination Image Reconstruction +1

Joint Super-Resolution and Alignment of Tiny Faces

1 code implementation19 Nov 2019 Yu Yin, Joseph P. Robinson, Yulun Zhang, Yun Fu

As for SR, the proposed method recovers sharper edges and more details from LR face images than other state-of-the-art methods, which we demonstrate qualitatively and quantitatively.


Visual Semantic Reasoning for Image-Text Matching

2 code implementations ICCV 2019 Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, Yun Fu

It outperforms the current best method by 6. 8% relatively for image retrieval and 4. 8% relatively for caption retrieval on MS-COCO (Recall@1 using 1K test set).

Image Retrieval Image-text matching +4

FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution

1 code implementation7 Jul 2019 Xiaole Zhao, Ying Liao, Tian He, Yulun Zhang, Yadong Wu, Tao Zhang

Most current image super-resolution (SR) methods based on convolutional neural networks (CNNs) use residual learning in network structural design, which favors to effective back propagation and hence improves SR performance by increasing model scale.

Image Super-Resolution

Multimodal Style Transfer via Graph Cuts

2 code implementations ICCV 2019 Yulun Zhang, Chen Fang, Yilin Wang, Zhaowen Wang, Zhe Lin, Yun Fu, Jimei Yang

An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices.

Style Transfer

Residual Non-local Attention Networks for Image Restoration

2 code implementations ICLR 2019 Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, Yun Fu

To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.

Demosaicking Image Denoising +1

TDAN: Temporally Deformable Alignment Network for Video Super-Resolution

2 code implementations7 Dec 2018 Yapeng Tian, Yulun Zhang, Yun Fu, Chenliang Xu

Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames).

Optical Flow Estimation Video Super-Resolution

Channel Splitting Network for Single MR Image Super-Resolution

no code implementations15 Oct 2018 Xiaole Zhao, Yulun Zhang, Tao Zhang, Xueming Zou

The proposed CSN model divides the hierarchical features into two branches, i. e., residual branch and dense branch, with different information transmissions.

Image Super-Resolution

Support Neighbor Loss for Person Re-Identification

1 code implementation18 Aug 2018 Kai Li, Zhengming Ding, Kunpeng Li, Yulun Zhang, Yun Fu

To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets.

Person Re-Identification

Residual Dense Network for Image Super-Resolution

16 code implementations CVPR 2018 Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu

In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers.

Color Image Denoising Image Super-Resolution

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