Search Results for author: Yu Tian

Found 83 papers, 43 papers with code

Strong and Weak Random Walks on Signed Networks

1 code implementation12 Jun 2024 Shazia'Ayn Babul, Yu Tian, Renaud Lambiotte

We show through a series of experiments on synthetic and empirical networks that the similarity matrix based on weak walks can be used for both unsupervised and semi-supervised clustering, outperforming the same similarity matrix based on strong walks when the graph has more than two communities, or exhibits asymmetry in the density of links.

Link Prediction

HPE-CogVLM: New Head Pose Grounding Task Exploration on Vision Language Model

no code implementations4 Jun 2024 Yu Tian, Tianqi Shao, Tsukasa Demizu, Xuyang Wu, Hsin-Tai Wu

In this paper, we present a novel framework to enhance the HPE prediction task by leveraging the visual grounding capability of CogVLM.

Head Pose Estimation Language Modelling +1

AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization

no code implementations30 May 2024 Jiawei Chen, Xiao Yang, Zhengwei Fang, Yu Tian, Yinpeng Dong, Zhaoxia Yin, Hang Su

Despite the widespread application of large language models (LLMs) across various tasks, recent studies indicate that they are susceptible to jailbreak attacks, which can render their defense mechanisms ineffective.

Sentence Sentence Compression

FairCLIP: Harnessing Fairness in Vision-Language Learning

1 code implementation CVPR 2024 Yan Luo, Min Shi, Muhammad Osama Khan, Muhammad Muneeb Afzal, Hao Huang, Shuaihang Yuan, Yu Tian, Luo Song, Ava Kouhana, Tobias Elze, Yi Fang, Mengyu Wang

Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions.

Fairness

VisionGPT: Vision-Language Understanding Agent Using Generalized Multimodal Framework

no code implementations14 Mar 2024 Chris Kelly, Luhui Hu, Bang Yang, Yu Tian, Deshun Yang, Cindy Yang, Zaoshan Huang, Zihao Li, Jiayin Hu, Yuexian Zou

With the emergence of large language models (LLMs) and vision foundation models, how to combine the intelligence and capacity of these open-sourced or API-available models to achieve open-world visual perception remains an open question.

Language Modelling Large Language Model +2

VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision Understanding

no code implementations14 Mar 2024 Chris Kelly, Luhui Hu, Jiayin Hu, Yu Tian, Deshun Yang, Bang Yang, Cindy Yang, Zihao Li, Zaoshan Huang, Yuexian Zou

It seamlessly integrates various SOTA vision models and brings the automation in the selection of SOTA vision models, identifies the suitable 3D mesh creation algorithms corresponding to 2D depth maps analysis, generates optimal results based on diverse multimodal inputs such as text prompts.

WorldGPT: A Sora-Inspired Video AI Agent as Rich World Models from Text and Image Inputs

no code implementations10 Mar 2024 Deshun Yang, Luhui Hu, Yu Tian, Zihao Li, Chris Kelly, Bang Yang, Cindy Yang, Yuexian Zou

Several text-to-video diffusion models have demonstrated commendable capabilities in synthesizing high-quality video content.

AI Agent Video Generation

Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters

no code implementations5 Mar 2024 Weizhi Wang, Khalil Mrini, Linjie Yang, Sateesh Kumar, Yu Tian, Xifeng Yan, Heng Wang

Our MLM filter can generalize to different models and tasks, and be used as a drop-in replacement for CLIPScore.

GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning

no code implementations26 Feb 2024 Hang Zou, Qiyang Zhao, Lina Bariah, Yu Tian, Mehdi Bennis, Samson Lasaulce, Merouane Debbah, Faouzi Bader

Connecting GenAI agents over a wireless network can potentially unleash the power of collective intelligence and pave the way for artificial general intelligence (AGI).

Transfer Learning

BSPA: Exploring Black-box Stealthy Prompt Attacks against Image Generators

no code implementations23 Feb 2024 Yu Tian, Xiao Yang, Yinpeng Dong, Heming Yang, Hang Su, Jun Zhu

It allows users to design specific prompts to generate realistic images through some black-box APIs.

A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation

no code implementations8 Feb 2024 Yu Tian, Ahmed Alhammadi, Abdullah Quran, Abubakar Sani Ali

In this paper, we address the intricate issue of RF signal separation by presenting a novel adaptation of the WaveNet architecture that introduces learnable dilation parameters, significantly enhancing signal separation in dense RF spectrums.

Data Augmentation

A minimal model of cognition based on oscillatory and reinforcement processes

no code implementations4 Feb 2024 Linnéa Gyllingberg, Yu Tian, David J. T. Sumpter

In the context of these findings, we discuss connections between our model and basal cognition in biological systems and slime moulds, in particular, how oscillations might contribute to self-organised problem-solving by these organisms.

Evil Geniuses: Delving into the Safety of LLM-based Agents

1 code implementation20 Nov 2023 Yu Tian, Xiao Yang, Jingyuan Zhang, Yinpeng Dong, Hang Su

Rapid advancements in large language models (LLMs) have revitalized in LLM-based agents, exhibiting impressive human-like behaviors and cooperative capabilities in various scenarios.

Specificity

UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized Multimodal Framework

1 code implementation16 Nov 2023 Chris Kelly, Luhui Hu, Cindy Yang, Yu Tian, Deshun Yang, Bang Yang, Zaoshan Huang, Zihao Li, Yuexian Zou

In the current landscape of artificial intelligence, foundation models serve as the bedrock for advancements in both language and vision domains.

FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling

1 code implementation3 Nov 2023 Yu Tian, Min Shi, Yan Luo, Ava Kouhana, Tobias Elze, Mengyu Wang

Existing medical fairness datasets are all for classification tasks, and no fairness datasets are available for medical segmentation, while medical segmentation is an equally important clinical task as classifications, which can provide detailed spatial information on organ abnormalities ready to be assessed by clinicians.

Fairness Image Segmentation +3

AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection

3 code implementations29 Oct 2023 Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen

It is a crucial task when training data is not accessible due to various concerns, eg, data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, and background features, such as defects/tumors on different products/organs, can vary significantly.

Anomaly Detection zero-shot anomaly detection +1

Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection

1 code implementation CVPR 2024 Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang

Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies, and 2) effectively generalize to unseen anomalies in new domains.

Supervised Anomaly Detection

FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling

no code implementations3 Oct 2023 Yan Luo, Muhammad Osama Khan, Yu Tian, Min Shi, Zehao Dou, Tobias Elze, Yi Fang, Mengyu Wang

To address this research gap, we conduct the first comprehensive study on the fairness of 3D medical imaging models across multiple protected attributes.

Fairness

The Devil is in the Details: A Deep Dive into the Rabbit Hole of Data Filtering

no code implementations27 Sep 2023 Haichao Yu, Yu Tian, Sateesh Kumar, Linjie Yang, Heng Wang

DataComp is a new benchmark dedicated to evaluating different methods for data filtering.

How Robust is Google's Bard to Adversarial Image Attacks?

1 code implementation21 Sep 2023 Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, Jun Zhu

By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability.

Adversarial Robustness Chatbot +1

Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction

1 code implementation21 Sep 2023 Yu Tian, Qiyang Zhao, Zine el abidine Kherroubi, Fouzi Boukhalfa, Kebin Wu, Faouzi Bader

Wireless communications at high-frequency bands with large antenna arrays face challenges in beam management, which can potentially be improved by multimodality sensing information from cameras, LiDAR, radar, and GPS.

Image Enhancement

Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning

no code implementations ICCV 2023 Yan Luo, Min Shi, Yu Tian, Tobias Elze, Mengyu Wang

This is the largest glaucoma detection dataset with 3D OCT imaging data and the first glaucoma progression forecasting dataset that is publicly available.

Fairness

Curvature-based Clustering on Graphs

no code implementations19 Jul 2023 Yu Tian, Zachary Lubberts, Melanie Weber

We consider several discrete curvature notions and analyze the utility of the resulting algorithms.

Clustering Community Detection +2

Structural Balance and Random Walks on Complex Networks with Complex Weights

no code implementations4 Jul 2023 Yu Tian, Renaud Lambiotte

Here, we focus on the case when the weight matrix is Hermitian, a reasonable assumption in many applications, and investigate both structural and dynamical properties of the complex-weighted networks.

Multi-Scenario Ranking with Adaptive Feature Learning

no code implementations29 Jun 2023 Yu Tian, Bofang Li, Si Chen, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng, Qian Wang, Chenliang Li

Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost.

Retrieval Transfer Learning

Large Generative AI Models for Telecom: The Next Big Thing?

no code implementations17 Jun 2023 Lina Bariah, Qiyang Zhao, Hang Zou, Yu Tian, Faouzi Bader, Merouane Debbah

To be specific, large GenAI models are envisioned to open up a new era of autonomous wireless networks, in which multi-modal GenAI models trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for building and training dedicated AI models for each specific task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks.

Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization

1 code implementation15 Jun 2023 Yan Luo, Yu Tian, Min Shi, Louis R. Pasquale, Lucy Q. Shen, Nazlee Zebardast, Tobias Elze, Mengyu Wang

To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection.

Fairness Feature Importance

Unleashing 3D Connectivity in Beyond 5G Networks with Reconfigurable Intelligent Surfaces

no code implementations8 May 2023 Jiguang He, Aymen Fakhreddine, Arthur S. de Sena, Yu Tian, Merouane Debbah

Reconfigurable intelligent surfaces (RISs) bring various benefits to the current and upcoming wireless networks, including enhanced spectrum and energy efficiency, soft handover, transmission reliability, and even localization accuracy.

Opportunities and challenges of ChatGPT for design knowledge management

no code implementations6 Apr 2023 Xin Hu, Yu Tian, Keisuke Nagato, Masayuki Nakao, Ang Liu

Recent advancements in Natural Language Processing have opened up new possibilities for the development of large language models like ChatGPT, which can facilitate knowledge management in the design process by providing designers with access to a vast array of relevant information.

Management

Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens

1 code implementation CVPR 2023 Yuxiao Chen, Jianbo Yuan, Yu Tian, Shijie Geng, Xinyu Li, Ding Zhou, Dimitris N. Metaxas, Hongxia Yang

However, direct aligning cross-modal information using such representations is challenging, as visual patches and text tokens differ in semantic levels and granularities.

Contrastive Learning

HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention

1 code implementation6 Mar 2023 Shijie Geng, Jianbo Yuan, Yu Tian, Yuxiao Chen, Yongfeng Zhang

The success of large-scale contrastive vision-language pretraining (CLIP) has benefited both visual recognition and multimodal content understanding.

BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete Annotations

no code implementations31 Jan 2023 Yuanhong Chen, Yuyuan Liu, Chong Wang, Michael Elliott, Chun Fung Kwok, Carlos Pena-Solorzano, Yu Tian, Fengbei Liu, Helen Frazer, Davis J. McCarthy, Gustavo Carneiro

Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it.

Lesion Detection

Learning Support and Trivial Prototypes for Interpretable Image Classification

1 code implementation ICCV 2023 Chong Wang, Yuyuan Liu, Yuanhong Chen, Fengbei Liu, Yu Tian, Davis J. McCarthy, Helen Frazer, Gustavo Carneiro

Prototypical part network (ProtoPNet) methods have been designed to achieve interpretable classification by associating predictions with a set of training prototypes, which we refer to as trivial prototypes because they are trained to lie far from the classification boundary in the feature space.

Explainable Artificial Intelligence (XAI) Image Classification +1

Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models

no code implementations26 Sep 2022 Chong Wang, Yuanhong Chen, Yuyuan Liu, Yu Tian, Fengbei Liu, Davis J. McCarthy, Michael Elliott, Helen Frazer, Gustavo Carneiro

On the other hand, prototype-based models improve interpretability by associating predictions with training image prototypes, but they are less accurate than global models and their prototypes tend to have poor diversity.

Knowledge Distillation

Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation

1 code implementation21 Sep 2022 Yuanhong Chen, Hu Wang, Chong Wang, Yu Tian, Fengbei Liu, Michael Elliott, Davis J. McCarthy, Helen Frazer, Gustavo Carneiro

When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-oblique (MLO) views.

Just Noticeable Difference Modeling for Face Recognition System

no code implementations13 Sep 2022 Yu Tian, Zhangkai Ni, Baoliang Chen, Shurun Wang, Shiqi Wang, Hanli Wang, Sam Kwong

In particular, in order to maximum redundancy removal without impairment of robust identity information, we apply the encoder with multiple feature extraction and attention-based feature decomposition modules to progressively decompose face features into two uncorrelated components, i. e., identity and residual features, via self-supervised learning.

Face Recognition Self-Supervised Learning

Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning

1 code implementation20 Jul 2022 Yuxiao Chen, Long Zhao, Jianbo Yuan, Yu Tian, Zhaoyang Xia, Shijie Geng, Ligong Han, Dimitris N. Metaxas

Despite the success of fully-supervised human skeleton sequence modeling, utilizing self-supervised pre-training for skeleton sequence representation learning has been an active field because acquiring task-specific skeleton annotations at large scales is difficult.

Action Detection Action Recognition +3

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 Image Segmentation +5

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.

Multiple Instance Learning Supervised Anomaly Detection +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, Mengyu Wang, 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

BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification

2 code implementations ICCV 2023 Yuanhong Chen, Fengbei Liu, Hu Wang, Chong Wang, Yu Tian, Yuyuan Liu, Gustavo Carneiro

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

Multi-Label Classification

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 implementation CVPR 2022 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 implementation CVPR 2022 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 Multi-Label Classification +1

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

3 code implementations24 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 Lost and Found (using extra training data)

Anomaly Detection Segmentation +1

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 Graph Neural Network +1

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

2 code implementations3 Sep 2021 Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro

Pre-training UAD methods with self-supervised learning, based on computer vision techniques, can mitigate this challenge, but they are sub-optimal because they do not explore domain knowledge for designing the pretext tasks, and their contrastive learning losses do not try to cluster the normal training images, which may result in a sparse distribution of normal images that is ineffective for anomaly detection.

Contrastive Learning Data Augmentation +2

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

Clustered Federated Learning via Generalized Total Variation Minimization

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

Our main analytic contribution is an upper bound on the deviation between the local model parameters learnt by our algorithm and an oracle-based clustered federated learning method.

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.

Position

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

NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification

1 code implementation6 Mar 2021 Fengbei Liu, Yuanhong Chen, Yu Tian, Yuyuan Liu, Chong Wang, Vasileios Belagiannis, Gustavo Carneiro

In this paper, we propose a new training module called Non-Volatile Unbiased Memory (NVUM), which non-volatility stores running average of model logits for a new regularization loss on noisy multi-label problem.

Image Classification with Label Noise Learning with noisy labels +1

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 +3

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.

Ranked #2 on Anomaly Detection on MNIST (using extra training data)

Classification One-Class Classification +1

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 +2

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).

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

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 Image to Video Generation +1

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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