Search Results for author: Junjun Jiang

Found 29 papers, 16 papers with code

Geometric Estimation via Robust Subspace Recovery

1 code implementation ECCV 2020 Aoxiang Fan, Xingyu Jiang, Yang Wang, Junjun Jiang, Jiayi Ma

Geometric estimation from image point correspondences is the core procedure of many 3D vision problems, which is prevalently accomplished by random sampling techniques.

Homography Estimation Pose Estimation

Towards End-to-End Image Compression and Analysis with Transformers

1 code implementation17 Dec 2021 Yuanchao Bai, Xu Yang, Xianming Liu, Junjun Jiang, YaoWei Wang, Xiangyang Ji, Wen Gao

Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction.

Image Classification Image Compression +1

Deep Attentional Guided Image Filtering

1 code implementation13 Dec 2021 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Xiangyang Ji

Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images.

Semantic Segmentation Super-Resolution

SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

1 code implementation9 Dec 2021 Zhenyu Li, Zehui Chen, Ang Li, Liangji Fang, Qinhong Jiang, Xianming Liu, Junjun Jiang, Bolei Zhou, Hang Zhao

To bridge this gap, we aim to learn a spatial-aware visual representation that can describe the three-dimensional space and is more suitable and effective for these tasks.

Contrastive Learning Unsupervised Pre-training

A Practical Contrastive Learning Framework for Single Image Super-Resolution

no code implementations27 Nov 2021 Gang Wu, Junjun Jiang, Xianming Liu, Jiayi Ma

Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer methods proposed for low-level tasks.

Contrastive Learning Image Super-Resolution

Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data

no code implementations23 Sep 2021 Jialei Xu, Yuanchao Bai, Xianming Liu, Junjun Jiang, Xiangyang Ji

In this paper, we propose a novel weakly-supervised framework to train a monocular depth estimation network to generate HR depth maps with resolution-mismatched supervision, i. e., the inputs are HR color images and the ground-truth are low-resolution (LR) depth maps.

Monocular Depth Estimation

TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network

no code implementations16 Sep 2021 Yuanzhi Wang, Tao Lu, Yanduo Zhang, Junjun Jiang, JiaMing Wang, Zhongyuan Wang, Jiayi Ma

Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e. g., facial parsing maps, facial landmarks) to focus on facial structure, thereby maintaining the consistency of the facial structure while restoring facial details.

Face Reconstruction Super-Resolution

Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution

no code implementations31 Aug 2021 Junjun Jiang, Chenyang Wang, Kui Jiang, Xianming Liu, Jiayi Ma

By this spectral splitting and aggregation strategy (SSAS), we can divide the original hyperspectral image into multiple samples to support the efficient training of the network and effectively exploit the spectral correlations among spectrum.

Image Super-Resolution

Learning with Noisy Labels via Sparse Regularization

1 code implementation ICCV 2021 Xiong Zhou, Xianming Liu, Chenyang Wang, Deming Zhai, Junjun Jiang, Xiangyang Ji

In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector.

Learning with noisy labels

Physics-Based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging

no code implementations CVPR 2021 Feilong Zhang, Xianming Liu, Cheng Guo, Shiyi Lin, Junjun Jiang, Xiangyang Ji

Specifically, we unfold the iterative process of the alternative projection phase retrieval into a feed-forward neural network, whose layers mimic the processing flow.

BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring

1 code implementation31 May 2021 Pengwei Liang, Junjun Jiang, Xianming Liu, Jiayi Ma

In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the DP data, which measures the defocus disparity between the left and right views.

Deblurring Meta-Learning

High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion

no code implementations4 Apr 2021 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Zhiwen Chen, Xiangyang Ji

Specifically, to effectively extract and combine relevant information from LR depth and HR guidance, we propose a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhance block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics.

Depth Map Super-Resolution

Omniscient Video Super-Resolution

no code implementations ICCV 2021 Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, Tao Lu, Xin Tian, Jiayi Ma

Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previously estimated SR output to help reconstruct the current frame recurrently.

Video Super-Resolution

Face Hallucination via Split-Attention in Split-Attention Network

1 code implementation22 Oct 2020 Tao Lu, Yuanzhi Wang, Yanduo Zhang, Yu Wang, Wei Liu, Zhongyuan Wang, Junjun Jiang

However, most of them fail to take into account the overall facial profile and fine texture details simultaneously, resulting in reduced naturalness and fidelity of the reconstructed face, and further impairing the performance of downstream tasks (e. g., face detection, facial recognition).

Face Detection Face Hallucination +3

Single Image Deraining via Scale-space Invariant Attention Neural Network

no code implementations9 Jun 2020 Bo Pang, Deming Zhai, Junjun Jiang, Xian-Ming Liu

Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems.

Image Enhancement Single Image Deraining

Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery

2 code implementations18 May 2020 Junjun Jiang, He Sun, Xian-Ming Liu, Jiayi Ma

Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs).

Hyperspectral Image Super-Resolution Image Super-Resolution

Multi-Scale Progressive Fusion Network for Single Image Deraining

1 code implementation CVPR 2020 Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, Junjun Jiang

In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal.

Single Image Deraining

Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis

no code implementations17 Mar 2020 Ruifeng Shi, Deming Zhai, Xian-Ming Liu, Junjun Jiang, Wen Gao

However, the performance of CNN-based classification approach depends on a large amount of high-quality manually labeled training data, which are inevitably introduced noise on labels in practice, leading to model overfitting and performance degradation.

General Classification Image Classification +1

ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising

no code implementations4 Mar 2020 Yongsen Zhao, Deming Zhai, Junjun Jiang, Xian-Ming Liu

Hyperspectral image (HSI) denoising is of crucial importance for many subsequent applications, such as HSI classification and interpretation.

Hyperspectral Image Denoising Image Denoising

Ensemble Super-Resolution with A Reference Dataset

1 code implementation12 May 2019 Junjun Jiang, Yi Yu, Zheng Wang, Suhua Tang, Ruimin Hu, Jiayi Ma

In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers).

Ensemble Learning Image Super-Resolution

Hyperspectral Image Classification in the Presence of Noisy Labels

1 code implementation12 Sep 2018 Junjun Jiang, Jiayi Ma, Zheng Wang, Chen Chen, Xian-Ming Liu

The key idea of RLPA is to exploit knowledge (e. g., the superpixel based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation.

General Classification Hyperspectral Image Classification

Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning

2 code implementations3 Sep 2018 Junjun Jiang, Yi Yu, Suhua Tang, Jiayi Ma, Akiko Aizawa, Kiyoharu Aizawa

To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL).

Face Hallucination

Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination

1 code implementation28 Jun 2018 Junjun Jiang, Yi Yu, Jinhui Hu, Suhua Tang, Jiayi Ma

Most of the current face hallucination methods, whether they are shallow learning-based or deep learning-based, all try to learn a relationship model between Low-Resolution (LR) and High-Resolution (HR) spaces with the help of a training set.

Face Hallucination Super-Resolution

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

1 code implementation26 Jun 2018 Junjun Jiang, Jiayi Ma, Chen Chen, Zhongyuan Wang, Zhihua Cai, Lizhe Wang

(1) Unlike the traditional PCA method based on a whole image, SuperPCA takes into account the diversity in different homogeneous regions, that is, different regions should have different projections.

Dimensionality Reduction General Classification

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