Search Results for author: Linlin Shen

Found 91 papers, 36 papers with code

Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation

no code implementations ECCV 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Recent generative adversarial network (GAN) based methods (e. g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation.

Domain Adaptation Generative Adversarial Network +4

Fingerprint Presentation Attack Detector Using Global-Local Model

no code implementations20 Feb 2024 Haozhe Liu, Wentian Zhang, Feng Liu, Haoqian Wu, Linlin Shen

While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained.

Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models

no code implementations17 Feb 2024 Wenxuan Wang, Yihang Su, Jingyuan Huan, Jie Liu, WenTing Chen, Yudi Zhang, Cheng-Yi Li, Kao-Jung Chang, Xiaohan Xin, Linlin Shen, Michael R. Lyu

However, these models are often evaluated on benchmarks that are unsuitable for the Med-MLLMs due to the intricate nature of the real-world diagnostic frameworks, which encompass diverse medical specialties and involve complex clinical decisions.

GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

no code implementations3 Feb 2024 Yaning Zhang, Zitong Yu, Xiaobin Huang, Linlin Shen, Jianfeng Ren

In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators.

Benchmarking DeepFake Detection +1

Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

1 code implementation18 Jan 2024 Songhe Deng, Wei Zhuo, Jinheng Xie, Linlin Shen

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels.

Contrastive Learning Prompt Engineering +4

SCPMan: Shape Context and Prior Constrained Multi-scale Attention Network for Pancreatic Segmentation

no code implementations26 Dec 2023 Leilei Zeng, Xuechen Li, Xinquan Yang, Linlin Shen, Song Wu

Specifically, we proposed a Multi-scale Feature Extraction Module (MFE) and a Mixed-scale Attention Integration Module (MAI) to address unclear pancreas boundaries.

Pancreas Segmentation Segmentation

Fine-Grained Image-Text Alignment in Medical Imaging Enables Cyclic Image-Report Generation

no code implementations13 Dec 2023 WenTing Chen, Linlin Shen, Xiang Li, Yixuan Yuan

To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process.

Language Modelling Large Language Model

TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

no code implementations10 Aug 2023 Xinquan Yang, Jinheng Xie, Xuechen Li, Xuguang Li, Linlin Shen, Yongqiang Deng

In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) which enables the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes.

Position

MRecGen: Multimodal Appropriate Reaction Generator

no code implementations5 Jul 2023 Jiaqi Xu, Cheng Luo, Weicheng Xie, Linlin Shen, Xiaofeng Liu, Lu Liu, Hatice Gunes, Siyang Song

Verbal and non-verbal human reaction generation is a challenging task, as different reactions could be appropriate for responding to the same behaviour.

TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

no code implementations26 Jun 2023 Xinquan Yang, Jinheng Xie, Xuguang Li, Xuechen Li, Xin Li, Linlin Shen, Yongqiang Deng

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available.

Position Position regression +1

ReactFace: Multiple Appropriate Facial Reaction Generation in Dyadic Interactions

1 code implementation25 May 2023 Cheng Luo, Siyang Song, Weicheng Xie, Micol Spitale, Linlin Shen, Hatice Gunes

ReactFace generates multiple different but appropriate photo-realistic human facial reactions by (i) learning an appropriate facial reaction distribution representing multiple appropriate facial reactions; and (ii) synchronizing the generated facial reactions with the speaker's verbal and non-verbal behaviours at each time stamp, resulting in realistic 2D facial reaction sequences.

VisorGPT: Learning Visual Prior via Generative Pre-Training

1 code implementation23 May 2023 Jinheng Xie, Kai Ye, Yudong Li, Yuexiang Li, Kevin Qinghong Lin, Yefeng Zheng, Linlin Shen, Mike Zheng Shou

Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet.

Image Generation Language Modelling +1

Two-Stream Regression Network for Dental Implant Position Prediction

no code implementations17 May 2023 Xinquan Yang, Xuguang Li, Xuechen Li, WenTing Chen, Linlin Shen, Xin Li, Yongqiang Deng

In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue.

Position Position regression +1

YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification

no code implementations7 May 2023 Lin Huang, Weisheng Li, Linlin Shen, Haojie Fu, Xue Xiao, Suihan Xiao

Maintaining inference speeds remarkably similar to those of the YOLOv5 model, the large, medium, and small YOLOCS models surpass the YOLOv5 model's AP by 1. 1%, 2. 3%, and 5. 2%, respectively.

object-detection Object Detection

Open-World Weakly-Supervised Object Localization

1 code implementation17 Apr 2023 Jinheng Xie, Zhaochuan Luo, Yuexiang Li, Haozhe Liu, Linlin Shen, Mike Zheng Shou

To handle such data, we propose a novel paradigm of contrastive representation co-learning using both labeled and unlabeled data to generate a complete G-CAM (Generalized Class Activation Map) for object localization, without the requirement of bounding box annotation.

Object Representation Learning +1

Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

1 code implementation19 Mar 2023 Zihan Wang, Siyang Song, Cheng Luo, Yuzhi Zhou, shiling Wu, Weicheng Xie, Linlin Shen

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW).

Graph Learning Graph Representation Learning

Precise Facial Landmark Detection by Reference Heatmap Transformer

no code implementations14 Mar 2023 Jun Wan, Jun Liu, Jie zhou, Zhihui Lai, Linlin Shen, Hang Sun, Ping Xiong, Wenwen Min

Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results.

Facial Landmark Detection

UniFace: Unified Cross-Entropy Loss for Deep Face Recognition

1 code implementation ICCV 2023 Jiancan Zhou, Xi Jia, Qiufu Li, Linlin Shen, Jinming Duan

To bridge this gap, we design a UCE (Unified Cross-Entropy) loss for face recognition model training, which is built on the vital constraint that all the positive sample-to-class similarities shall be larger than the negative ones.

Face Recognition TAR

StyleGene: Crossover and Mutation of Region-Level Facial Genes for Kinship Face Synthesis

1 code implementation CVPR 2023 Hao Li, Xianxu Hou, Zepeng Huang, Linlin Shen

As cycle-like losses are designed to measure the L_2 distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i. e. no paired kinship face data is required.

Kinship face generation Kinship Verification

Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition

no code implementations ICCV 2023 Xilin He, Qinliang Lin, Cheng Luo, Weicheng Xie, Siyang Song, Feng Liu, Linlin Shen

Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i. e., they make predictions mainly based on texture cues.

Object Recognition

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities

3 code implementations13 Dec 2022 Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Guo, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.

Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation

no code implementations9 Dec 2022 Wei Chen, Xi Jia, Zhongqun Zhang, Hyung Jin Chang, Linlin Shen, Jinming Duan, Ales Leonardis

The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task.

6D Pose Estimation using RGB Data Augmentation

GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features

1 code implementation19 Nov 2022 Siyang Song, Yuxin Song, Cheng Luo, Zhiyuan Song, Selim Kuzucu, Xi Jia, Zhijiang Guo, Weicheng Xie, Linlin Shen, Hatice Gunes

Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data.

Graph Representation Learning

ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

no code implementations29 Oct 2022 Xinquan Yang, Xuguang Li, Xuechen Li, Peixi Wu, Linlin Shen, Yongqiang Deng

In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data.

Position Position regression +1

SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic Segmentation

1 code implementation26 Oct 2022 Junliang Chen, Xiaodong Zhao, Cheng Luo, Linlin Shen

Recent mainstream weakly supervised semantic segmentation (WSSS) approaches are mainly based on Class Activation Map (CAM) generated by a CNN (Convolutional Neural Network) based image classifier.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation

no code implementations22 Oct 2022 Junliang Chen, Xiaodong Zhao, Minmin Liu, Linlin Shen

Recent mainstream weakly-supervised semantic segmentation (WSSS) approaches mainly relies on image-level classification learning, which has limited representation capacity.

Segmentation Weakly supervised Semantic Segmentation +1

A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays

1 code implementation5 Sep 2022 Haoqin Ji, Haozhe Liu, Yuexiang Li, Jinheng Xie, Nanjun He, Yawen Huang, Dong Wei, Xinrong Chen, Linlin Shen, Yefeng Zheng

Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost.

Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator

no code implementations12 Aug 2022 Xiangbo Gao, Cheng Luo, Qinliang Lin, Weicheng Xie, Minmin Liu, Linlin Shen, Keerthy Kusumam, Siyang Song

\noindent Traditional L_p norm-restricted image attack algorithms suffer from poor transferability to black box scenarios and poor robustness to defense algorithms.

Adversarial Attack Image Classification +4

Sample hardness based gradient loss for long-tailed cervical cell detection

no code implementations7 Aug 2022 Minmin Liu, Xuechen Li, Xiangbo Gao, Junliang Chen, Linlin Shen, Huisi Wu

Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution.

Cell Detection object-detection +1

Activation Template Matching Loss for Explainable Face Recognition

no code implementations5 Jul 2022 Huawei Lin, Haozhe Liu, Qiufu Li, Linlin Shen

Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets?

Face Alignment Face Recognition +2

Delving into the Scale Variance Problem in Object Detection

no code implementations16 Jun 2022 Junliang Chen, Xiaodong Zhao, Linlin Shen

For most of the single-stage object detectors, replacing the traditional convolutions with MSConvs in the detection head can bring more than 2. 5\% improvement in AP (on COCO 2017 dataset), with only 3\% increase of FLOPs.

Object object-detection +1

Selective Multi-Scale Learning for Object Detection

no code implementations16 Jun 2022 Junliang Chen, Weizeng Lu, Linlin Shen

When integrated with SMSL, two-stage detectors can get around 1. 0\% improvement in AP.

Object object-detection +1

Robust Representation via Dynamic Feature Aggregation

1 code implementation16 May 2022 Haozhe Liu, Haoqin Ji, Yuexiang Li, Nanjun He, Haoqian Wu, Feng Liu, Linlin Shen, Yefeng Zheng

With the regularization and orthogonal classifier, a more compact embedding space can be obtained, which accordingly improves the model robustness against adversarial attacks.

Out of Distribution (OOD) Detection

Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition

2 code implementations2 May 2022 Cheng Luo, Siyang Song, Weicheng Xie, Linlin Shen, Hatice Gunes

While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display.

Facial Action Unit Detection Relation

Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation

2 code implementations25 Mar 2022 Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, Linlin Shen

While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.

Contrastive Learning Image Classification +3

Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity

1 code implementation CVPR 2022 Cheng Luo, Qinliang Lin, Weicheng Xie, Bizhu Wu, Jinheng Xie, Linlin Shen

Current adversarial attack research reveals the vulnerability of learning-based classifiers against carefully crafted perturbations.

Adversarial Attack Semantic Similarity +1

Cross Language Image Matching for Weakly Supervised Semantic Segmentation

2 code implementations5 Mar 2022 Jinheng Xie, Xianxu Hou, Kai Ye, Linlin Shen

As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.

Object Weakly supervised Semantic Segmentation +1

FEAT: Face Editing with Attention

no code implementations6 Feb 2022 Xianxu Hou, Linlin Shen, Or Patashnik, Daniel Cohen-Or, Hui Huang

In this paper, we build on the StyleGAN generator, and present a method that explicitly encourages face manipulation to focus on the intended regions by incorporating learned attention maps.

Disentanglement

C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation

1 code implementation CVPR 2022 Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, Linlin Shen

While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.

Contrastive Learning Image Classification +3

CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation

no code implementations CVPR 2022 Jinheng Xie, Xianxu Hou, Kai Ye, Linlin Shen

As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.

Object Weakly supervised Semantic Segmentation +1

Gated SwitchGAN for multi-domain facial image translation

no code implementations28 Nov 2021 Xiaokang Zhang, Yuanlue Zhu, WenTing Chen, Wenshuang Liu, Linlin Shen

The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation.

Attribute feature selection +3

Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising

1 code implementation15 Nov 2021 Feng Liu, Zhe Kong, Haozhe Liu, Wentian Zhang, Linlin Shen

The proposed method learns important features of fingerprint images by weighing the importance of each channel and identifying discriminative channels and "noise" channels.

Denoising

Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition

no code implementations26 Oct 2021 Siyang Song, Zilong Shao, Shashank Jaiswal, Linlin Shen, Michel Valstar, Hatice Gunes

This approach builds on two following findings in cognitive science: (i) human cognition partially determines expressed behaviour and is directly linked to true personality traits; and (ii) in dyadic interactions individuals' nonverbal behaviours are influenced by their conversational partner behaviours.

Neural Architecture Search

Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization

1 code implementation ICCV 2021 Jinheng Xie, Cheng Luo, Xiangping Zhu, Ziqi Jin, Weizeng Lu, Linlin Shen

In the first stage, an activation map generator produces activation maps based on the low-level feature maps in the classifier, such that rich contextual object information is included in an online manner.

Object Weakly-Supervised Object Localization

Manifold-preserved GANs

no code implementations18 Sep 2021 Haozhe Liu, Hanbang Liang, Xianxu Hou, Haoqian Wu, Feng Liu, Linlin Shen

Generative Adversarial Networks (GANs) have been widely adopted in various fields.

Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

1 code implementation9 Sep 2021 Zhe Kong, Wentian Zhang, Feng Liu, Wenhan Luo, Haozhe Liu, Linlin Shen, Raghavendra Ramachandra

Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem.

Self-Supervised Learning

WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification

2 code implementations28 Jul 2021 Qiufu Li, Linlin Shen, Sheng Guo, Zhihui Lai

We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet).

Adversarial Robustness Image Classification

Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis

no code implementations23 Jun 2021 Zhongliang Li, Zhihao Jin, Xuechen Li, Linlin Shen

The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data.

Computed Tomography (CT) COVID-19 Diagnosis +2

Self-Attention Based Text Knowledge Mining for Text Detection

1 code implementation CVPR 2021 Qi Wan, Haoqin Ji, Linlin Shen

Considering the importance of exploring text contents for text detection, we propose STKM (Self-attention based Text Knowledge Mining), which consists of a CNN Encoder and a Self-attention Decoder, to learn general prior knowledge for text detection from SynthText.

Text Detection

Neuron segmentation using 3D wavelet integrated encoder-decoder network

1 code implementation1 Jun 2021 Qiufu Li, Linlin Shen

Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers.

Segmentation

Learning a Model-Driven Variational Network for Deformable Image Registration

no code implementations25 May 2021 Xi Jia, Alexander Thorley, Wei Chen, Huaqi Qiu, Linlin Shen, Iain B Styles, Hyung Jin Chang, Ales Leonardis, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert, Jinming Duan

We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).

Denoising Image Registration

Group-wise Inhibition based Feature Regularization for Robust Classification

1 code implementation ICCV 2021 Haozhe Liu, Haoqian Wu, Weicheng Xie, Feng Liu, Linlin Shen

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e. g. corrupted and adversarial samples).

Classification Domain Generalization +2

MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures

1 code implementation26 Feb 2021 Luyan Liu, Zhiwei Wen, Songwei Liu, Hong-Yu Zhou, Hongwei Zhu, Weicheng Xie, Linlin Shen, Kai Ma, Yefeng Zheng

Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.

Image Segmentation Medical Image Segmentation +2

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

3 code implementations24 Feb 2021 Bing Li, Yuanlue Zhu, Yitong Wang, Chia-Wen Lin, Bernard Ghanem, Linlin Shen

Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photo-face.

Face Generation Translation

GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing

no code implementations22 Dec 2020 Xianxu Hou, Xiaokang Zhang, Linlin Shen, Zhihui Lai, Jun Wan

Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there still lacks of control over the generation process in order to achieve semantic face editing.

Attribute Image Generation

HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation

no code implementations12 Dec 2020 Saqib Qamar, Parvez Ahmad, Linlin Shen

Preliminary results on the BRATS 2020 testing set show that achieved by our proposed approach, the dice (DSC) scores of ET, WT, and TC are 0. 79457, 0. 87494, and 0. 83712, respectively.

Brain Tumor Segmentation Decision Making +2

Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Network

no code implementations16 Nov 2020 Jun Wan, Zhihui Lai, Linlin Shen, Jie zhou, Can Gao, Gang Xiao, Xianxu Hou

Moreover, a novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection.

Facial Landmark Detection

Context Aware 3D UNet for Brain Tumor Segmentation

no code implementations25 Oct 2020 Parvez Ahmad, Saqib Qamar, Linlin Shen, Adnan Saeed

UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation.

Brain Tumor Segmentation Segmentation +1

Think about boundary: Fusing multi-level boundary information for landmark heatmap regression

no code implementations25 Aug 2020 Jinheng Xie, Jun Wan, Linlin Shen, Zhihui Lai

Although current face alignment algorithms have obtained pretty good performances at predicting the location of facial landmarks, huge challenges remain for faces with severe occlusion and large pose variations, etc.

Face Alignment regression

TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification

no code implementations29 Jul 2020 Wenting Chen, Shuang Yu, Junde Wu, Kai Ma, Cheng Bian, Chunyan Chu, Linlin Shen, Yefeng Zheng

A topology ranking discriminator based on ordinal regression is proposed to rank the topological connectivity level of the ground-truth, the generated A/V mask and the intentionally shuffled mask.

Classification General Classification +1

Translate the Facial Regions You Like Using Region-Wise Normalization

no code implementations29 Jul 2020 Wenshuang Liu, Wenting Chen, Linlin Shen

We propose in this paper a region-wise normalization framework, for region level face translation.

Image Generation MORPH +1

Instance-aware Self-supervised Learning for Nuclei Segmentation

no code implementations22 Jul 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.

Instance Segmentation Segmentation +2

MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint

no code implementations22 Jul 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models.

Domain Adaptation Generative Adversarial Network +3

Geometry Constrained Weakly Supervised Object Localization

1 code implementation ECCV 2020 Weizeng Lu, Xi Jia, Weicheng Xie, Linlin Shen, Yicong Zhou, Jinming Duan

The detector predicts the object location defined by a set of coefficients describing a geometric shape (i. e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator.

Object Weakly-Supervised Object Localization

WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation

no code implementations29 May 2020 Qiufu Li, Linlin Shen

In deep networks, the lost data details significantly degrade the performances of image segmentation.

Image Segmentation Segmentation +1

Wavelet Integrated CNNs for Noise-Robust Image Classification

1 code implementation CVPR 2020 Qiufu Li, Linlin Shen, Sheng Guo, Zhihui Lai

The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets.

Classification General Classification +1

Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks

no code implementations5 Apr 2020 Pourya Shamsolmoali, Masoumeh Zareapoor, Linlin Shen, Abdul Hamid Sadka, Jie Yang

It improves learning from imbalanced data by incorporating the majority distribution structure in the generation of new minority samples.

Inferring Dynamic Representations of Facial Actions from a Still Image

no code implementations4 Apr 2019 Siyang Song, Enrique Sánchez-Lozano, Linlin Shen, Alan Johnston, Michel Valstar

We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation.

Test

Texture Deformation Based Generative Adversarial Networks for Face Editing

no code implementations24 Dec 2018 WenTing Chen, Xinpeng Xie, Xi Jia, Linlin Shen

We also evaluate our approach qualitatively and quantitatively on facial attribute and facial expression synthesis.

Attribute Image-to-Image Translation +1

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

A Self-Organizing Tensor Architecture for Multi-View Clustering

no code implementations18 Oct 2018 Lifang He, Chun-Ta Lu, Yong Chen, Jiawei Zhang, Linlin Shen, Philip S. Yu, Fei Wang

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.

Clustering

Reversed Active Learning based Atrous DenseNet for Pathological Image Classification

no code implementations6 Jul 2018 Yuexiang Li, Xinpeng Xie, Linlin Shen, Shaoxiong Liu

However, the usage of deep learning networks for the pathological image analysis encounters several challenges, e. g. high resolution (gigapixel) of pathological images and lack of annotations of cancer areas.

Active Learning Classification +2

Active Learning for Breast Cancer Identification

no code implementations18 Apr 2018 Xinpeng Xie, Yuexiang Li, Linlin Shen

Our RAL is applied to the training set of a simple convolutional neural network (CNN) to remove mislabeled images.

Active Learning

Kernelized Support Tensor Machines

no code implementations ICML 2017 Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks.

Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification

no code implementations CVPR 2017 Lifang He, Chun-Ta Lu, Hao Ding, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.

Classification General Classification

Skin Lesion Classification using Class Activation Map

no code implementations3 Mar 2017 Xi Jia, Linlin Shen

We proposed a two stage framework with only one network to analyze skin lesion images, we firstly trained a convolutional network to classify these images, and cropped the import regions which the network has the maximum activation value.

Classification General Classification +2

Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network

no code implementations2 Mar 2017 Yuexiang Li, Linlin Shen

In this paper, we proposed two deep learning methods to address all the three tasks announced in ISIC 2017, i. e. lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3).

General Classification Lesion Classification +2

Deep Feature Consistent Variational Autoencoder

14 code implementations2 Oct 2016 Xianxu Hou, Linlin Shen, Ke Sun, Guoping Qiu

We present a novel method for constructing Variational Autoencoder (VAE).

Attribute Style Transfer

Object Specific Deep Learning Feature and Its Application to Face Detection

no code implementations6 Sep 2016 Xianxu Hou, Ke Sun, Linlin Shen, Guoping Qiu

We present a method for discovering and exploiting object specific deep learning features and use face detection as a case study.

Face Detection Object

Latent Constrained Correlation Filters for Object Localization

no code implementations7 Jun 2016 Shangzhen Luan, Baochang Zhang, Jungong Han, Chen Chen, Ling Shao, Alessandro Perina, Linlin Shen

There is a neglected fact in the traditional machine learning methods that the data sampling can actually lead to the solution sampling.

Object Object Localization

LOAD: Local Orientation Adaptive Descriptor for Texture and Material Classification

no code implementations22 Apr 2015 Xianbiao Qi, Guoying Zhao, Linlin Shen, Qingquan Li, Matti Pietikainen

It is worth to mention that we achieve a 65. 4\% classification accuracy-- which is, to the best of our knowledge, the highest record by far --on Flickr Material Database by using a single feature.

General Classification Material Classification +2

Cannot find the paper you are looking for? You can Submit a new open access paper.