Search Results for author: Yu Tian

Found 38 papers, 21 papers with code

When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation

1 code implementation3 May 2022 Yu Tian, Jianxin Chang, Yannan Niu, Yang song, Chenliang Li

Specifically, multi-interest methods such as ComiRec and MIMN, focus on extracting different interests for a user by performing historical item clustering, while graph convolution methods including TGSRec and SURGE elect to refine user preferences based on multi-level correlations between historical items.

Sequential Recommendation

Translation Consistent Semi-supervised Segmentation for 3D Medical Images

1 code implementation28 Mar 2022 Yuyuan Liu, Yu Tian, Chong Wang, Yuanhong Chen, Fengbei Liu, Vasileios Belagiannis, Gustavo Carneiro

The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data.

Brain Tumor Segmentation Translation +1

Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

1 code implementation23 Mar 2022 Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan W Verjans, Gustavo Carneiro

Current polyp detection methods from colonoscopy videos use exclusively normal (i. e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps.

Anomaly Detection Frame +1

Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

1 code implementation22 Mar 2022 Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W Verjans, Gustavo Carneiro

Our UAD approach, the memory-augmented multi-level cross-attentional masked autoencoder (MemMC-MAE), is a transformer-based approach, consisting of a novel memory-augmented self-attention operator for the encoder and a new multi-level cross-attention operator for the decoder.

Image Reconstruction Unsupervised Anomaly Detection

Semantic-guided Image Virtual Attribute Learning for Noisy Multi-label Chest X-ray Classification

no code implementations3 Mar 2022 Yuanhong Chen, Fengbei Liu, Yu Tian, Yuyuan Liu, Gustavo Carneiro

Deep learning methods have shown outstanding classification accuracy in medical image analysis problems, which is largely attributed to the availability of large datasets manually annotated with clean labels.

Generalized Visual Quality Assessment of GAN-Generated Face Images

no code implementations28 Jan 2022 Yu Tian, Zhangkai Ni, Baoliang Chen, Shiqi Wang, Hanli Wang, Sam Kwong

However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model.

Face Generation Image Quality Assessment +1

Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

1 code implementation25 Nov 2021 Yuyuan Liu, Yu Tian, Yuanhong Chen, Fengbei Liu, Vasileios Belagiannis, Gustavo Carneiro

The accurate prediction by this model allows us to use a challenging combination of network, input data and feature perturbations to improve the consistency learning generalisation, where the feature perturbations consist of a new adversarial perturbation.

Semi-Supervised Semantic Segmentation

ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification

1 code implementation25 Nov 2021 Fengbei Liu, Yu Tian, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, Gustavo Carneiro

Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e. g., lesion classification) and multi-label (e. g., multiple-disease diagnosis) problems, and 2) handle imbalanced learning (because of the high variance in disease prevalence).

Image Classification Lesion Classification +2

Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes

1 code implementation24 Nov 2021 Yu Tian, Yuyuan Liu, Guansong Pang, Fengbei Liu, Yuanhong Chen, Gustavo Carneiro

However, previous uncertainty approaches that directly associate high uncertainty to anomaly may sometimes lead to incorrect anomaly predictions, and external reconstruction models tend to be too inefficient for real-time self-driving embedded systems.

Ranked #2 on Anomaly Detection on Fishyscapes L&F (using extra training data)

Anomaly Detection Semantic Segmentation

AE-StyleGAN: Improved Training of Style-Based Auto-Encoders

1 code implementation17 Oct 2021 Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min, Ruijiang Gao, Yu Tian, Dimitris Metaxas

StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space.

Vision-Aided Beam Tracking: Explore the Proper Use of Camera Images with Deep Learning

no code implementations29 Sep 2021 Yu Tian, Chenwei Wang

We investigate the problem of wireless beam tracking on mmWave bands with the assistance of camera images.

Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation

no code implementations28 Sep 2021 Yiyu Liu, Qian Liu, Yu Tian, Changping Wang, Yanan Niu, Yang song, Chenliang Li

In this paper, we propose a novel concept-aware denoising graph neural network (named CONDE) for micro-video recommendation.

Denoising Recommendation Systems

Deep Learning Model for Demodulation Reference Signal based Channel Estimation

no code implementations22 Sep 2021 Yu Tian, Chengguang Li, Sen yang

In this paper, we propose a deep learning model for Demodulation Reference Signal (DMRS) based channel estimation task.

Self-supervised Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images

1 code implementation3 Sep 2021 Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro

MSACL is based on a novel optimisation to contrast normal and multiple classes of synthetised abnormal images, with each class enforced to form a tight and dense cluster in terms of Euclidean distance and cosine similarity, where abnormal images are formed by simulating a varying number of lesions of different sizes and appearance in the normal images.

Contrastive Learning Data Augmentation +1

Dual Projection Generative Adversarial Networks for Conditional Image Generation

1 code implementation ICCV 2021 Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos, Yu Tian, Ruijiang Gao, Asim Kadav, Dimitris Metaxas

We then propose an improved cGAN model with Auxiliary Classification that directly aligns the fake and real conditionals $P(\text{class}|\text{image})$ by minimizing their $f$-divergence.

Conditional Image Generation

Networked Federated Multi-Task Learning

1 code implementation26 May 2021 Yasmin SarcheshmehPour, Yu Tian, Linli Zhang, Alexander Jung

These local datasets are often related via an intrinsic network structure that arises from domain-specific notions of similarity between local datasets.

Distributed Computing Edge-computing +2

RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments

no code implementations15 May 2021 Yu Tian, Shuai Yuan, Weisheng Chen, Naijin Liu

Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely recognized as an enabling technology for improving radio spectrum efficiency.

Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing

no code implementations30 Apr 2021 TianHao Li, Yu Tian, Shuai Yuan, Naijin Liu

In this paper, a novel bandwidth negotiation mechanism is proposed for massive devices wireless spectrum sharing, in which individual device locally negotiates bandwidth usage with neighbor devices and globally optimal spectrum utilization is achieved through distributed decision-making.

Decision Making Distributed Optimization +2

Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands

no code implementations17 Apr 2021 Jing Xu, Yu Tian, Shuai Yuan, Naijin Liu

In this paper, a noise attention method is proposed for unsupervised spectrum anomaly detection in unauthorized bands.

Anomaly Detection

Noisy Label Learning for Large-scale Medical Image Classification

no code implementations6 Mar 2021 Fengbei Liu, Yu Tian, Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

The classification accuracy of deep learning models depends not only on the size of their training sets, but also on the quality of their labels.

General Classification Image Classification

Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images

1 code implementation5 Mar 2021 Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i. e., healthy) images to detect any abnormal (i. e., unhealthy) samples that do not conform to the expected normal patterns.

Contrastive Learning Representation Learning +1

Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification

1 code implementation5 Mar 2021 Fengbei Liu, Yu Tian, Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

In this paper, we propose Self-supervised Mean Teacher for Semi-supervised (S$^2$MTS$^2$) learning that combines self-supervised mean-teacher pre-training with semi-supervised fine-tuning.

Contrastive Learning General Classification +2

Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning

3 code implementations ICCV 2021 Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro

To address this issue, we introduce a novel and theoretically sound method, named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature magnitude learning function to effectively recognise the positive instances, substantially improving the robustness of the MIL approach to the negative instances from abnormal videos.

Anomaly Detection In Surveillance Videos Contrastive Learning +1

Deep One-Class Classification via Interpolated Gaussian Descriptor

2 code implementations25 Jan 2021 Yuanhong Chen, Yu Tian, Guansong Pang, Gustavo Carneiro

The adversarial interpolation is enforced to consistently learn a smooth Gaussian descriptor, even when the training data is small or contaminated with anomalous samples.

Unsupervised Anomaly Detection

On NOMA-Based mmWave Communications

no code implementations15 Sep 2020 Yu Tian, Gaofeng Pan, Mohamed-Slim

Two power allocation strategies are considered: the first one is a general (fixed) power allocation scheme under which we derive the OP and EC of NOMA users in closed form; the other one is an optimal power allocation scheme that can achieve the maximum sum rate for the whole system.

Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy

1 code implementation26 Jun 2020 Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro

Anomaly detection methods generally target the learning of a normal image distribution (i. e., inliers showing healthy cases) and during testing, samples relatively far from the learned distribution are classified as anomalies (i. e., outliers showing disease cases).

Few Shot Anomaly Detection

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

no code implementations10 Jun 2020 Yu Tian, Gaofeng Pan, Mohamed-Slim Alouini

To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios.

Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario

no code implementations18 Mar 2020 Yu Tian, Kunbo Zhang, Leyuan Wang, Zhenan Sun

Extensive experiments demonstrate the advantages of the PAAS technique to counter diverse face spoofing attacks (print, replay, mask) in uncontrolled indoor and outdoor conditions by learning polarized face images of 33 people.

Face Anti-Spoofing

Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images

no code implementations9 Jan 2020 Ruigang Niu, Xian Sun, Yu Tian, Wenhui Diao, Kaiqiang Chen, Kun fu

Semantic segmentation in very high resolution (VHR) aerial images is one of the most challenging tasks in remote sensing image understanding.

Semantic Segmentation

Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames

no code implementations23 Oct 2019 Yuyuan Liu, Yu Tian, Gabriel Maicas, Leonardo Z. C. T. Pu, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro

We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.

Anomaly Detection Frame

Learning to Forecast and Refine Residual Motion for Image-to-Video Generation

1 code implementation ECCV 2018 Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris Metaxas

We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object.

Human Pose Forecasting Translation +1

CR-GAN: Learning Complete Representations for Multi-view Generation

1 code implementation28 Jun 2018 Yu Tian, Xi Peng, Long Zhao, Shaoting Zhang, Dimitris N. Metaxas

Generating multi-view images from a single-view input is an essential yet challenging problem.

Self-Supervised Learning

A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation

no code implementations5 Jan 2018 Yunyi Liang, Zhiyong Cui, Yu Tian, Huimiao Chen, Yinhai Wang

The GAA is able to combine traffic flow theory with neural networks and thus improve the accuracy of traffic state estimation.

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