Search Results for author: Jun Yu

Found 102 papers, 35 papers with code

Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts

no code implementations3 Dec 2023 Eashan Adhikarla, Kai Zhang, Jun Yu, Lichao Sun, John Nicholson, Brian D. Davison

As a result, it raises concerns about the overall robustness of the machine learning techniques for computer vision applications that are deployed publicly for consumers.

Data Augmentation Transfer Learning

Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection

no code implementations30 Nov 2023 Jianjian Qin, Chunzhi Gu, Jun Yu, Chao Zhang

To fully exploit saliency guidance, on each map, we select a pixel pair from the cluster with the highest centroid saliency to form a patch pair.

Anomaly Detection Self-Supervised Learning

Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset

no code implementations7 Nov 2023 Jianjian Qin, Chunzhi Gu, Jun Yu, Chao Zhang

We present PD-REAL, a novel large-scale dataset for unsupervised anomaly detection (AD) in the 3D domain.

Unsupervised Anomaly Detection

On the Onset of Robust Overfitting in Adversarial Training

no code implementations1 Oct 2023 Chaojian Yu, Xiaolong Shi, Jun Yu, Bo Han, Tongliang Liu

Adversarial Training (AT) is a widely-used algorithm for building robust neural networks, but it suffers from the issue of robust overfitting, the fundamental mechanism of which remains unclear.

Adversarial Robustness Data Augmentation

A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization

no code implementations14 Sep 2023 Yu Ding, Jun Yu, Chunzhi Gu, Shangce Gao, Chao Zhang

Recently, a novel mathematical ANN model, known as the dendritic neuron model (DNM), has been proposed to address nonlinear problems by more accurately reflecting the structure of real neurons.

Multi-class Classification

Regularly Truncated M-estimators for Learning with Noisy Labels

1 code implementation2 Sep 2023 Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Tongliang Liu

However, such a procedure is arguably debatable from two folds: (a) it does not consider the bad influence of noisy labels in selected small-loss examples; (b) it does not make good use of the discarded large-loss examples, which may be clean or have meaningful information for generalization.

Learning with noisy labels

Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels

no code implementations11 Jul 2023 Hui Kang, Sheng Liu, Huaxi Huang, Jun Yu, Bo Han, Dadong Wang, Tongliang Liu

In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data.

Learning with noisy labels

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision

no code implementations12 Jun 2023 Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu

Training a classifier exploiting a huge amount of supervised data is expensive or even prohibited in a situation, where the labeling cost is high.

Binary Classification Pseudo Label

Importance Sparsification for Sinkhorn Algorithm

1 code implementation11 Jun 2023 Mengyu Li, Jun Yu, Tao Li, Cheng Meng

Sinkhorn algorithm has been used pervasively to approximate the solution to optimal transport (OT) and unbalanced optimal transport (UOT) problems.

BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks

1 code implementation26 May 2023 Kai Zhang, Jun Yu, Zhiling Yan, Yixin Liu, Eashan Adhikarla, Sunyang Fu, Xun Chen, Chen Chen, Yuyin Zhou, Xiang Li, Lifang He, Brian D. Davison, Quanzheng Li, Yong Chen, Hongfang Liu, Lichao Sun

In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse datasets to accept multi-modal inputs and perform a range of downstream tasks.

Image Captioning Medical Visual Question Answering +2

Multi-task Paired Masking with Alignment Modeling for Medical Vision-Language Pre-training

no code implementations13 May 2023 Ke Zhang, Yan Yang, Jun Yu, Hanliang Jiang, Jianping Fan, Qingming Huang, Weidong Han

To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge.

ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos

1 code implementation CVPR 2023 Zhou Yu, Lixiang Zheng, Zhou Zhao, Fei Wu, Jianping Fan, Kui Ren, Jun Yu

A recent benchmark AGQA poses a promising paradigm to generate QA pairs automatically from pre-annotated scene graphs, enabling it to measure diverse reasoning abilities with granular control.

Question Answering Spatio-temporal Scene Graphs +1

A Comparison of Image Denoising Methods

1 code implementation18 Apr 2023 Zhaoming Kong, Fangxi Deng, Haomin Zhuang, Jun Yu, Lifang He, Xiaowei Yang

In this paper, to investigate the applicability of existing denoising techniques, we compare a variety of denoising methods on both synthetic and real-world datasets for different applications.

Benchmarking Image Denoising

SAR2EO: A High-resolution Image Translation Framework with Denoising Enhancement

no code implementations8 Apr 2023 Jun Yu, Shenshen Du, Guochen Xie, Renjie Lu, Pengwei Li, Zhongpeng Cai, Keda Lu

Synthetic Aperture Radar (SAR) to electro-optical (EO) image translation is a fundamental task in remote sensing that can enrich the dataset by fusing information from different sources.

Denoising Translation +1

Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

2 code implementations CVPR 2023 Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu

Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains.

A Dual Branch Network for Emotional Reaction Intensity Estimation

no code implementations16 Mar 2023 Jun Yu, Jichao Zhu, Wangyuan Zhu, Zhongpeng Cai, Guochen Xie, Renda Li, Gongpeng Zhao

Emotional Reaction Intensity(ERI) estimation is an important task in multimodal scenarios, and has fundamental applications in medicine, safe driving and other fields.


Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition

no code implementations15 Mar 2023 Jun Yu, Zhongpeng Cai, Renda Li, Gongpeng Zhao, Guochen Xie, Jichao Zhu, Wangyuan Zhu

Facial Expression Recognition (FER) is an important task in computer vision and has wide applications in human-computer interaction, intelligent security, emotion analysis, and other fields.

Emotion Recognition Face Recognition +2

GLOW: Global Layout Aware Attacks on Object Detection

no code implementations27 Feb 2023 Buyu Liu, BaoJun, Jianping Fan, Xi Peng, Kui Ren, Jun Yu

More desired attacks, to this end, should be able to fool defenses with such consistency checks.

object-detection Object Detection

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

no code implementations18 Feb 2023 Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.

Graph Learning Language Modelling +1

ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories

no code implementations5 Feb 2023 Zijian Zhang, Zhou Zhao, Jun Yu, Qi Tian

In this paper, we propose a novel and flexible conditional diffusion model by introducing conditions into the forward process.

Denoising Image Generation

Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples

no code implementations ICCV 2023 Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu

As selected data have high discrepancies in probabilities, the divergence of two networks can be maintained by training on such data.

Learning with noisy labels

Graph Matching with Bi-level Noisy Correspondence

3 code implementations ICCV 2023 Yijie Lin, Mouxing Yang, Jun Yu, Peng Hu, Changqing Zhang, Xi Peng

In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC).

Contrastive Learning Graph Learning +1

Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples

no code implementations31 Oct 2022 Jianjian Qin, Chunzhi Gu, Jun Yu, Chao Zhang

Moreover, our method only requires very few normal samples to train the student network due to the teacher-student distillation mechanism.

3D Anomaly Detection Transfer Learning

Micro Expression Generation with Thin-plate Spline Motion Model and Face Parsing

3 code implementations MM '22: Proceedings of the 30th ACM International Conference on Multimedia 2022 Jun Yu, Guochen Xie, Zhongpeng Cai, Peng He, Fang Gao, Qiang Ling

We (Team: USTC-IAT-United) also compare our method with other competitors' in MEGC2022, and the expert evaluation results show that our method performs best, which verifies the effectiveness of our method.

Face Parsing Micro-expression Generation +2

Strength-Adaptive Adversarial Training

no code implementations4 Oct 2022 Chaojian Yu, Dawei Zhou, Li Shen, Jun Yu, Bo Han, Mingming Gong, Nannan Wang, Tongliang Liu

Firstly, applying a pre-specified perturbation budget on networks of various model capacities will yield divergent degree of robustness disparity between natural and robust accuracies, which deviates from robust network's desideratum.

Adversarial Robustness Scheduling

Pseudo-Label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection

1 code implementation Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022 Jun Yu, Liwen Zhang, Shenshen Du, Hao Chang, Keda Lu, Zhong Zhang, Ye Yu, Lei Wang, Qiang Ling

To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images.

Data Augmentation object-detection +3

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

1 code implementation23 Sep 2022 Jun Yu, Zhaoming Kong, Liang Zhan, Li Shen, Lifang He

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task.

feature selection regression

High-Performance Transmission Mechanism Design of Multi-Stream Carrier Aggregation for 5G Non-Standalone Network

no code implementations21 Aug 2022 Jun Yu, Shunqing Zhang, Jiayun Sun, Shugong Xu, Shan Cao

Multi-stream carrier aggregation is a key technology to expand bandwidth and improve the throughput of the fifth-generation wireless communication systems.

A viable framework for semi-supervised learning on realistic dataset

2 code implementations Machine Learning 2022 Hao Chang, Guochen Xie, Jun Yu, Qiang Ling, Fang Gao, Ye Yu

Semi-supervised Fine-Grained Recognition is a challenging task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.

Learning Disentangled Representations for Controllable Human Motion Prediction

no code implementations4 Jul 2022 Chunzhi Gu, Jun Yu, Chao Zhang

Specifically, the inductive bias imposed by the extra CVAE path encourages two latent variables in two paths to respectively govern separate representations for each partial-body motion.

Human motion prediction Inductive Bias +1

Understanding Robust Overfitting of Adversarial Training and Beyond

1 code implementation17 Jun 2022 Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu

Here, we explore the causes of robust overfitting by comparing the data distribution of \emph{non-overfit} (weak adversary) and \emph{overfitted} (strong adversary) adversarial training, and observe that the distribution of the adversarial data generated by weak adversary mainly contain small-loss data.

Adversarial Robustness Data Ablation

An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation

no code implementations31 May 2022 Jingyi Zhang, Cheng Meng, Jun Yu, Mengrui Zhang, Wenxuan Zhong, Ping Ma

Theoretically, we show the selected subsample can be used for efficient density estimation by deriving the convergence rate for the proposed subsample kernel density estimator.

Active Learning Density Estimation +1

Hilbert Curve Projection Distance for Distribution Comparison

1 code implementation30 May 2022 Tao Li, Cheng Meng, Hongteng Xu, Jun Yu

Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling.

Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification

1 code implementation26 May 2022 Mengyu Li, Jun Yu, Hongteng Xu, Cheng Meng

As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs.


Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification

2 code implementations4 May 2022 Jun Yu, Hao Chang, Keda Lu, Liwen Zhang, Shenshen Du, Zhong Zhang

Multi-modal aerial view object classification (MAVOC) in Automatic target recognition (ATR), although an important and challenging problem, has been under studied.

Clustering Image Classification

Multi-model Ensemble Learning Method for Human Expression Recognition

no code implementations28 Mar 2022 Jun Yu, Zhongpeng Cai, Peng He, Guocheng Xie, Qiang Ling

Moreover, we introduce the multi-fold ensemble method to train and ensemble several models with the same architecture but different data distributions to enhance the performance of our solution.

Ensemble Learning

Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering

1 code implementation24 Mar 2022 Zhou Yu, Zitian Jin, Jun Yu, Mingliang Xu, Hongbo Wang, Jianping Fan

Recent advances in Transformer architectures [1] have brought remarkable improvements to visual question answering (VQA).

Question Answering Visual Question Answering

Hyper-relationship Learning Network for Scene Graph Generation

no code implementations15 Feb 2022 Yibing Zhan, Zhi Chen, Jun Yu, Baosheng Yu, DaCheng Tao, Yong Luo

As a result, HLN significantly improves the performance of scene graph generation by integrating and reasoning from object interactions, relationship interactions, and transitive inference of hyper-relationships.

Graph Attention Graph Generation +1

Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks

1 code implementation CVPR 2022 Wenwen Pan, Haonan Shi, Zhou Zhao, Jieming Zhu, Xiuqiang He, Zhigeng Pan, Lianli Gao, Jun Yu, Fei Wu, Qi Tian

Audio-Guided video semantic segmentation is a challenging problem in visual analysis and editing, which automatically separates foreground objects from background in a video sequence according to the referring audio expressions.

Denoising Segmentation +3

Uncertainty Set Prediction of Aggregated Wind Power Generation based on Bayesian LSTM and Spatio-Temporal Analysis

no code implementations7 Oct 2021 Xiaopeng Li, Jiang Wu, Zhanbo Xu, Kun Liu, Jun Yu, Xiaohong Guan

This paper focuses on the uncertainty set prediction of the aggregated generation of geographically distributed wind farms.

Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples

no code implementations29 Sep 2021 Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu

The sample selection approach is popular in learning with noisy labels, which tends to select potentially clean data out of noisy data for robust training.

Learning with noisy labels

ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration

1 code implementation16 Aug 2021 Yuhao Cui, Zhou Yu, Chunqi Wang, Zhongzhou Zhao, Ji Zhang, Meng Wang, Jun Yu

Nevertheless, most existing VLP approaches have not fully utilized the intrinsic knowledge within the image-text pairs, which limits the effectiveness of the learned alignments and further restricts the performance of their models.

Visual Reasoning

BiSTF: Bilateral-Branch Self-Training Framework for Semi-Supervised Large-scale Fine-Grained Recognition

no code implementations14 Jul 2021 Hao Chang, Guochen Xie, Jun Yu, Qiang Ling

Semi-supervised Fine-Grained Recognition is a challenge task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.

A Weakly-Supervised Depth Estimation Network Using Attention Mechanism

no code implementations10 Jul 2021 Fang Gao, Jiabao Wang, Jun Yu, Yaoxiong Wang, Feng Shuang

It consists of a dense residual network structure, an adaptive weight channel attention (AWCA) module, a patch second non-local (PSNL) module and a soft label generation method.

Monocular Depth Estimation Scene Understanding

The Story in Your Eyes: An Individual-difference-aware Model for Cross-person Gaze Estimation

1 code implementation27 Jun 2021 Jun Bao, Buyu Liu, Jun Yu

We propose a novel method on refining cross-person gaze prediction task with eye/face images only by explicitly modelling the person-specific differences.

Gaze Estimation Gaze Prediction +1

Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training

no code implementations10 Jun 2021 Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Jun Yu, Xiaoyu Wang, Tongliang Liu

However, pre-processing methods may suffer from the robustness degradation effect, in which the defense reduces rather than improving the adversarial robustness of a target model in a white-box setting.

Adversarial Defense Adversarial Robustness

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels

no code implementations NeurIPS 2021 Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama

In this way, we also give large-loss but less selected data a try; then, we can better distinguish between the cases (a) and (b) by seeing if the losses effectively decrease with the uncertainty after the try.

Learning with noisy labels

Instance Correction for Learning with Open-set Noisy Labels

no code implementations1 Jun 2021 Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama

Lots of approaches, e. g., loss correction and label correction, cannot handle such open-set noisy labels well, since they need training data and test data to share the same label space, which does not hold for learning with open-set noisy labels.

Removing Adversarial Noise in Class Activation Feature Space

no code implementations ICCV 2021 Dawei Zhou, Nannan Wang, Chunlei Peng, Xinbo Gao, Xiaoyu Wang, Jun Yu, Tongliang Liu

Then, we train a denoising model to minimize the distances between the adversarial examples and the natural examples in the class activation feature space.

Adversarial Robustness Denoising

Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing

1 code implementation25 Dec 2020 Jun Yu, Hao Zhou, Yibing Zhan, DaCheng Tao

Essentially, DGCPN addresses the inaccurate similarity problem by exploring and exploiting the data's intrinsic relationships in a graph.

Quantization Retrieval

Sufficient dimension reduction for classification using principal optimal transport direction

1 code implementation NeurIPS 2020 Cheng Meng, Jun Yu, Jingyi Zhang, Ping Ma, Wenxuan Zhong

The proposed method, named principal optimal transport direction (POTD), estimates the basis of the SDR subspace using the principal directions of the optimal transport coupling between the data respecting different response categories.

Classification Dimensionality Reduction +1

Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples

no code implementations21 Aug 2020 Jinfeng Li, Weifeng Liu, Yicong Zhou, Jun Yu, Dapeng Tao

Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain.

Domain Adaptation Graph Learning +1

Retrieval of Family Members Using Siamese Neural Network

no code implementations30 May 2020 Jun Yu, Guochen Xie, Mengyan Li, Xinlong Hao

While in inference procedure, we try another similarity computing method by dropping the followed several fully connected layers and directly computing the cosine similarity of the two feature vectors.


Deep Fusion Siamese Network for Automatic Kinship Verification

no code implementations30 May 2020 Jun Yu, Mengyan Li, Xinlong Hao, Guochen Xie

Recognizing Families In the Wild (RFIW) is a challenging kinship recognition task with multiple tracks, which is based on Families in the Wild (FIW), a large-scale and comprehensive image database for automatic kinship recognition.

Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data

no code implementations21 May 2020 Jun Yu, HaiYing Wang, Mingyao Ai, Huiming Zhang

We first derive optimal Poisson subsampling probabilities in the context of quasi-likelihood estimation under the A- and L-optimality criteria.

Deep Multimodal Neural Architecture Search

1 code implementation25 Apr 2020 Zhou Yu, Yuhao Cui, Jun Yu, Meng Wang, DaCheng Tao, Qi Tian

Most existing works focus on a single task and design neural architectures manually, which are highly task-specific and hard to generalize to different tasks.

Image-text matching Neural Architecture Search +4

Repulsive Mixture Models of Exponential Family PCA for Clustering

no code implementations7 Apr 2020 Maoying Qiao, Tongliang Liu, Jun Yu, Wei Bian, DaCheng Tao

To alleviate this problem, in this paper, a repulsiveness-encouraging prior is introduced among mixing components and a diversified EPCA mixture (DEPCAM) model is developed in the Bayesian framework.


Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approach

no code implementations6 Apr 2020 Maoying Qiao, Jun Yu, Wei Bian, DaCheng Tao

Specifically, an HMRNet is reorganized into a hierarchical structure with homogeneous networks as its layers and heterogeneous links connecting them.

Community Detection

Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos

no code implementations16 Mar 2020 Yijun Song, Jingwen Wang, Lin Ma, Zhou Yu, Jun Yu

The task of temporally grounding textual queries in videos is to localize one video segment that semantically corresponds to the given query.

Multimodal Unified Attention Networks for Vision-and-Language Interactions

no code implementations12 Aug 2019 Zhou Yu, Yuhao Cui, Jun Yu, DaCheng Tao, Qi Tian

Learning an effective attention mechanism for multimodal data is important in many vision-and-language tasks that require a synergic understanding of both the visual and textual contents.

Question Answering Visual Grounding +1

Deep Modular Co-Attention Networks for Visual Question Answering

7 code implementations CVPR 2019 Zhou Yu, Jun Yu, Yuhao Cui, DaCheng Tao, Qi Tian

In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth.

Question Answering Test +1

Effective degrees of freedom for surface finish defect detection and classification

no code implementations20 Jun 2019 Natalya Pya Arnqvist, Blaise Ngendangenzwa, Eric Lindahl, Leif Nilsson, Jun Yu

One of the primary concerns of product quality control in the automotive industry is an automated detection of defects of small sizes on specular car body surfaces.

Defect Detection General Classification

Multimodal Transformer with Multi-View Visual Representation for Image Captioning

no code implementations20 May 2019 Jun Yu, Jing Li, Zhou Yu, Qingming Huang

Despite the success of existing studies, current methods only model the co-attention that characterizes the inter-modal interactions while neglecting the self-attention that characterizes the intra-modal interactions.

Image Captioning Machine Translation

Grand Challenge of 106-Point Facial Landmark Localization

no code implementations9 May 2019 Yinglu Liu, Hao Shen, Yue Si, Xiaobo Wang, Xiangyu Zhu, Hailin Shi, Zhibin Hong, Hanqi Guo, Ziyuan Guo, Yanqin Chen, Bi Li, Teng Xi, Jun Yu, Haonian Xie, Guochen Xie, Mengyan Li, Qing Lu, Zengfu Wang, Shenqi Lai, Zhenhua Chai, Xiaoming Wei

However, previous competitions on facial landmark localization (i. e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components.

Face Alignment Face Recognition +2

Local Deep-Feature Alignment for Unsupervised Dimension Reduction

no code implementations22 Apr 2019 Jian Zhang, Jun Yu, DaCheng Tao

Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features.

Clustering Data Visualization +1

Single Pixel Reconstruction for One-stage Instance Segmentation

no code implementations16 Apr 2019 Jun Yu, Jinghan Yao, Jian Zhang, Zhou Yu, DaCheng Tao

In this paper, we propose a one-stage framework, SPRNet, which performs efficient instance segmentation by introducing a single pixel reconstruction (SPR) branch to off-the-shelf one-stage detectors.

Instance Segmentation Region Proposal +2

Adapting Stochastic Block Models to Power-Law Degree Distributions

no code implementations5 Apr 2019 Maoying Qiao, Jun Yu, Wei Bian, Qiang Li, DaCheng Tao

Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data.

Cross-modal Subspace Learning via Kernel Correlation Maximization and Discriminative Structure Preserving

no code implementations26 Mar 2019 Jun Yu, Xiao-Jun Wu

Our model not only considers the inter-modality correlation by maximizing the kernel correlation but also preserves the semantically structural information within each modality.

Semantic Similarity Semantic Textual Similarity

Unsupervised Multi-modal Hashing for Cross-modal retrieval

no code implementations26 Mar 2019 Jun Yu, Xiao-Jun Wu

With the advantage of low storage cost and high efficiency, hashing learning has received much attention in the domain of Big Data.

Content-Based Image Retrieval Cross-Modal Retrieval +2

Discriminative Supervised Hashing for Cross-Modal similarity Search

no code implementations6 Dec 2018 Jun Yu, Xiao-Jun Wu, Josef Kittler

With the advantage of low storage cost and high retrieval efficiency, hashing techniques have recently been an emerging topic in cross-modal similarity search.

Cross-Modal Retrieval Retrieval

Textually Guided Ranking Network for Attentional Image Retweet Modeling

no code implementations24 Oct 2018 Zhou Zhao, Hanbing Zhan, Lingtao Meng, Jun Xiao, Jun Yu, Min Yang, Fei Wu, Deng Cai

In this paper, we study the problem of image retweet prediction in social media, which predicts the image sharing behavior that the user reposts the image tweets from their followees.

Semi-supervised Hashing for Semi-Paired Cross-View Retrieval

no code implementations19 Jun 2018 Jun Yu, Xiao-Jun Wu, Josef Kittler

Recently, hashing techniques have gained importance in large-scale retrieval tasks because of their retrieval speed.


Deep Boosting of Diverse Experts

no code implementations ICLR 2018 Wei Zhang, Qiuyu Chen, Jun Yu, Jianping Fan

In this paper, a deep boosting algorithm is developed to learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs (base experts) with diverse capabilities, e. g., these base deep CNNs are sequentially trained to recognize a set of object classes in an easy-to-hard way according to their learning complexities.

Object Recognition

Multi-modal Face Pose Estimation with Multi-task Manifold Deep Learning

no code implementations18 Dec 2017 Chaoqun Hong, Jun Yu

In the proposed deep learning based framework, Manifold Regularized Convolutional Layers (MRCL) improve traditional convolutional layers by learning the relationship among outputs of neurons.

Descriptive Multi-Task Learning +2

Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs

2 code implementations4 Dec 2017 Jun Yu, Xingxin Xu, Fei Gao, Shengjie Shi, Meng Wang, DaCheng Tao, Qingming Huang

Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data.

 Ranked #1 on Face Sketch Synthesis on CUFS (FID metric)

Face Sketch Synthesis

Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering

2 code implementations10 Aug 2017 Zhou Yu, Jun Yu, Chenchao Xiang, Jianping Fan, DaCheng Tao

For fine-grained image and question representations, a `co-attention' mechanism is developed by using a deep neural network architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations.

Question Answering Visual Question Answering +1

Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering

6 code implementations ICCV 2017 Zhou Yu, Jun Yu, Jianping Fan, DaCheng Tao

For multi-modal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches.

Question Answering Visual Question Answering

Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition

no code implementations8 Jul 2017 Tianyi Zhao, Baopeng Zhang, Wei zhang, Ning Zhou, Jun Yu, Jianping Fan

Our LMM model can provide an end-to-end approach for jointly learning: (a) the deep networks to extract more discriminative deep features for image and object class representation; (b) the tree classifier for recognizing large numbers of object classes hierarchically; and (c) the visual hierarchy adaptation for achieving more accurate indexing of large numbers of object classes hierarchically.

Object Recognition

Deep Mixture of Diverse Experts for Large-Scale Visual Recognition

no code implementations24 Jun 2017 Tianyi Zhao, Jun Yu, Zhenzhong Kuang, Wei zhang, Jianping Fan

In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task spaces), e. g., such base deep CNNs are trained to recognize different subsets of tens of thousands of atomic object classes.

Multi-Task Learning Object Recognition

Constrained Low-Rank Learning Using Least Squares-Based Regularization

no code implementations15 Nov 2016 Ping Li, Jun Yu, Meng Wang, Luming Zhang, Deng Cai, Xuelong. Li

To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization.

General Classification Image Categorization +2

Bayesian and empirical Bayesian forests

no code implementations8 Feb 2015 Matt Taddy, Chun-Sheng Chen, Jun Yu, Mitch Wyle

We derive ensembles of decision trees through a nonparametric Bayesian model, allowing us to view random forests as samples from a posterior distribution.


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