Search Results for author: Gang Yang

Found 18 papers, 9 papers with code

Segmentation-based Information Extraction and Amalgamation in Fundus Images for Glaucoma Detection

no code implementations23 Sep 2022 Yanni Wang, Gang Yang, Dayong Ding, Jianchun Zao

Glaucoma is a severe blinding disease, for which automatic detection methods are urgently needed to alleviate the scarcity of ophthalmologists.

Decision Making

Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction

1 code implementation15 Sep 2022 Gang Yang, Li Zhang, Man Zhou, Aiping Liu, Xun Chen, Zhiwei Xiong, Feng Wu

Interpretable neural network models are of significant interest since they enhance the trustworthiness required in clinical practice when dealing with medical images.


TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection

1 code implementation4 Jul 2022 Chang Liu, Gang Yang, Shuo Wang, Hangxu Wang, Yunhua Zhang, Yutao Wang

We employ the powerful feature extraction capability of Transformer (PVTv2) to extract global semantic information from RGB data and design a lightweight CNN backbone (LWDepthNet) to extract spatial structure information from depth data without pre-training.

object-detection RGB-D Salient Object Detection +1

Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach

no code implementations15 May 2022 Mengwei Yuan, Gang Yang, Shijie Song, Luping Zhou, Robert Minasian, Xiaoke Yi

The correlation coefficient of the prediction by the presented PTCN model remains greater than 0. 974 even when the size of training data is decreased to 17%.

Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening

1 code implementation CVPR 2022 Gang Yang, Man Zhou, Keyu Yan, Aiping Liu, Xueyang Fu, Fan Wang

Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success.


Unfolding Taylor's Approximations for Image Restoration

no code implementations NeurIPS 2021 Man Zhou, Zeyu Xiao, Xueyang Fu, Aiping Liu, Gang Yang, Zhiwei Xiong

Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image.

Image Restoration

Unsupervised Domain Expansion for Visual Categorization

2 code implementations1 Apr 2021 Jie Wang, Kaibin Tian, Dayong Ding, Gang Yang, Xirong Li

In this paper we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model's performance on the source domain.

Knowledge Distillation Unsupervised Domain Adaptation +1

SEA: Sentence Encoder Assembly for Video Retrieval by Textual Queries

1 code implementation24 Nov 2020 Xirong Li, Fangming Zhou, Chaoxi Xu, Jiaqi Ji, Gang Yang

Inspired by the initial success of previously few works in combining multiple sentence encoders, this paper takes a step forward by developing a new and general method for effectively exploiting diverse sentence encoders.

Ranked #2 on Ad-hoc video search on TRECVID-AVS16 (IACC.3) (using extra training data)

Ad-hoc video search Management +5

Dual Encoding for Video Retrieval by Text

1 code implementation10 Sep 2020 Jianfeng Dong, Xirong Li, Chaoxi Xu, Xun Yang, Gang Yang, Xun Wang, Meng Wang

In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own.

Ranked #3 on Ad-hoc video search on TRECVID-AVS16 (IACC.3) (using extra training data)

Ad-hoc video search Retrieval +1

Feature Re-Learning with Data Augmentation for Video Relevance Prediction

1 code implementation8 Apr 2020 Jianfeng Dong, Xun Wang, Leimin Zhang, Chaoxi Xu, Gang Yang, Xirong Li

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval.

Data Augmentation Retrieval

Automatically Generating Macro Research Reports from a Piece of News

no code implementations21 Nov 2019 Wenxin Hu, Xiaofeng Zhang, Gang Yang

As we all know, it requires the macro analysts to write such reports within a short period of time after the important economic news are released.

Text Generation

Hierarchical Attention Networks for Medical Image Segmentation

no code implementations20 Nov 2019 Fei Ding, Gang Yang, Jinlu Liu, Jun Wu, Dayong Ding, Jie Xv, Gangwei Cheng, Xirong Li

Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.

Image Segmentation Medical Image Segmentation +1

Imagination Based Sample Construction for Zero-Shot Learning

no code implementations29 Oct 2018 Gang Yang, Jinlu Liu, Xirong Li

Different from these existing types of methods, we propose a new method: sample construction to deal with the problem of ZSL.

Association Image Retrieval +2

Detect Globally, Refine Locally: A Novel Approach to Saliency Detection

no code implementations CVPR 2018 Tiantian Wang, Lihe Zhang, Shuo Wang, Huchuan Lu, Gang Yang, Xiang Ruan, Ali Borji

Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position.

object-detection RGB Salient Object Detection +2

COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval

2 code implementations22 May 2018 Xirong Li, Chaoxi Xu, Xiaoxu Wang, Weiyu Lan, Zhengxiong Jia, Gang Yang, Jieping Xu

This paper contributes to cross-lingual image annotation and retrieval in terms of data and baseline methods.


Adaptive Tag Selection for Image Annotation

no code implementations17 Sep 2014 Xixi He, Xirong Li, Gang Yang, Jieping Xu, Qin Jin

The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth.


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