Search Results for author: Yanyun Qu

Found 21 papers, 5 papers with code

LatticeNet: Towards Lightweight Image Super-resolution with Lattice Block

no 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

Towards Compact Single Image Super-Resolution via Contrastive Self-distillation

1 code implementation25 May 2021 Yanbo Wang, Shaohui Lin, Yanyun Qu, Haiyan Wu, Zhizhong Zhang, Yuan Xie, Angela Yao

Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but often require sophisticated architectures with heavy memory cost and computational overhead, significantly restricts their practical deployments on resource-limited devices.

Image Super-Resolution SSIM +1

Contrastive Learning for Compact Single Image Dehazing

2 code implementations CVPR 2021 Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, Lizhuang Ma

In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively.

Contrastive Learning Image Dehazing +1

Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

1 code implementation CVPR 2021 Xudong Tian, Zhizhong Zhang, Shaohui Lin, Yanyun Qu, Yuan Xie, Lizhuang Ma

The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy.

Cross-Modality Person Re-identification Cross-Modal Person Re-Identification +3

Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds

no code implementations7 Jan 2021 Jingyu Gong, Jiachen Xu, Xin Tan, Jie zhou, Yanyun Qu, Yuan Xie, Lizhuang Ma

Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation where ambiguous features might be generated in feature extraction, leading to misclassification in the transition area between two objects.

Point Cloud Segmentation Semantic Segmentation

Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation

no code implementations ICCV 2021 Yachao Zhang, Yanyun Qu, Yuan Xie, Zonghao Li, Shanshan Zheng, Cuihua Li

In this way, the graph topology of the whole point cloud can be effectively established by the introduced auxiliary supervision, such that the information propagation between the labeled and unlabeled points will be realized.

Self-Supervised Learning Semantic Segmentation

Meta Segmentation Network for Ultra-Resolution Medical Images

no code implementations19 Feb 2020 Tong Wu, Yuan Xie, Yanyun Qu, Bicheng Dai, Shuxin Chen

MSN can fast generate the weights of fusion layers through a simple meta-learner, requiring only a few training samples and epochs to converge.

Meta-Learning Semantic Segmentation

Novelty Detection via Non-Adversarial Generative Network

no code implementations3 Feb 2020 Chengwei Chen, Wang Yuan, Yuan Xie, Yanyun Qu, Yiqing Tao, Haichuan Song, Lizhuang Ma

One-class novelty detection is the process of determining if a query example differs from the training examples (the target class).

Image Reconstruction

Enhanced Pix2pix Dehazing Network

no code implementations CVPR 2019 Yanyun Qu, Yizi Chen, Jingying Huang, Yuan Xie

Inspired by visual perception global-first theory, the discriminator guides the generator to create a pseudo realistic image on a coarse scale, while the enhancer following the generator is required to produce a realistic dehazing image on the fine scale.

Image Dehazing Image-to-Image Translation +2

Bi-GANs-ST for Perceptual Image Super-resolution

no code implementations1 Nov 2018 Xiaotong Luo, Rong Chen, Yuan Xie, Yanyun Qu, Cuihua Li

In this paper, motivated by [1], we aim to generate a high-quality SR result which balances between the two indices, i. e., the perception index and root-mean-square error (RMSE).

Image Super-Resolution SSIM

Jointly Deep Multi-View Learning for Clustering Analysis

no code implementations19 Aug 2018 Bingqian Lin, Yuan Xie, Yanyun Qu, Cuihua Li, Xiaodan Liang

To our best knowledge, this is the first work to model the multi-view clustering in a deep joint framework, which will provide a meaningful thinking in unsupervised multi-view learning.

MULTI-VIEW LEARNING

Robust Kernelized Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization

no code implementations15 Sep 2017 Yanyun Qu, Jinyan Liu, Yuan Xie, Wensheng Zhang

In particular, the original tensor-based multi-view self-representation clustering problem is a special case of our approach and can be solved by our algorithm.

Face Clustering

On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization

no code implementations23 Oct 2016 Yuan Xie, DaCheng Tao, Wensheng Zhang, Lei Zhang, Yan Liu, Yanyun Qu

Different from traditional unfolding based tensor norm, this low-rank tensor constraint has optimality properties similar to that of matrix rank derived from SVD, so the complementary information among views can be explored more efficiently and thoroughly.

Multi-view Subspace Clustering

Distortion-driven Turbulence Effect Removal using Variational Model

no code implementations17 Jan 2014 Yuan Xie, Wensheng Zhang, DaCheng Tao, Wenrui Hu, Yanyun Qu, Hanzi Wang

To solve, or at least reduce these effects, we propose a new scheme to recover a latent image from observed frames by integrating a new variational model and distortion-driven spatial-temporal kernel regression.

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