Search Results for author: Yizhou Yu

Found 140 papers, 63 papers with code

Propagating Over Phrase Relations for One-Stage Visual Grounding

no code implementations ECCV 2020 Sibei Yang, Guanbin Li, Yizhou Yu

Phrase level visual grounding aims to locate in an image the corresponding visual regions referred to by multiple noun phrases in a given sentence.

Phrase Grounding Relational Reasoning +2

Uncertainty Estimation of Large Language Models in Medical Question Answering

no code implementations11 Jul 2024 Jiaxin Wu, Yizhou Yu, Hong-Yu Zhou

In this work, we benchmark popular UE methods with different model sizes on medical question-answering datasets.

Question Answering Text Generation

Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation

no code implementations3 Jun 2024 Fei Gao, Siwen Wang, Fandong Zhang, Hong-Yu Zhou, Yizhou Wang, Churan Wang, Gang Yu, Yizhou Yu

This transformation enables seamless integration of 2D and 3D data, and facilitates cross-dimensional self-supervised learning for 3D medical image analysis.

3D Classification Representation Learning +1

OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation

no code implementations CVPR 2024 Ganlong Zhao, Guanbin Li, Weikai Chen, Yizhou Yu

Recent advances in Iterative Vision-and-Language Navigation (IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes.

Vision and Language Navigation

DreamDA: Generative Data Augmentation with Diffusion Models

1 code implementation19 Mar 2024 Yunxiang Fu, Chaoqi Chen, Yu Qiao, Yizhou Yu

The acquisition of large-scale, high-quality data is a resource-intensive and time-consuming endeavor.

Data Augmentation Diversity

RegionGPT: Towards Region Understanding Vision Language Model

no code implementations CVPR 2024 Qiushan Guo, Shalini De Mello, Hongxu Yin, Wonmin Byeon, Ka Chun Cheung, Yizhou Yu, Ping Luo, Sifei Liu

Vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vision encoder, and the use of coarse-grained training data that lacks detailed, region-specific captions.

Language Modelling

SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging

no code implementations27 Feb 2024 Meng Lou, Hanning Ying, Xiaoqing Liu, Hong-Yu Zhou, Yuqing Zhang, Yizhou Yu

This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion classification in 3D multi-phase CT and MR imaging with varying phase counts.

Computational Efficiency Lesion Classification

Progressive Conservative Adaptation for Evolving Target Domains

no code implementations7 Feb 2024 Gangming Zhao, Chaoqi Chen, Wenhao He, Chengwei Pan, Chaowei Fang, Jinpeng Li, Xilin Chen, Yizhou Yu

Moreover, as adjusting to the most recent target domain can interfere with the features learned from previous target domains, we develop a conservative sparse attention mechanism.

Domain Adaptation Meta-Learning +1

Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining

1 code implementation5 Feb 2024 Jiarun Liu, Hao Yang, Hong-Yu Zhou, Yan Xi, Lequan Yu, Yizhou Yu, Yong Liang, Guangming Shi, Shaoting Zhang, Hairong Zheng, Shanshan Wang

However, it is challenging for existing methods to model long-range global information, where convolutional neural networks (CNNs) are constrained by their local receptive fields, and vision transformers (ViTs) suffer from high quadratic complexity of their attention mechanism.

Image Segmentation Medical Image Segmentation +1

Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models

1 code implementation22 Jan 2024 Chenyu Lian, Hong-Yu Zhou, Yizhou Yu, Liansheng Wang

Parameter-efficient fine-tuning (PEFT) that was initially developed for exploiting pre-trained large language models has recently emerged as an effective approach to perform transfer learning on computer vision tasks.

Transfer Learning

Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation

no code implementations21 Jan 2024 Jichang Li, Guanbin Li, Yizhou Yu

Once the graph has been refined, Adaptive Betweenness Clustering is introduced to facilitate semantic transfer by using across-domain betweenness clustering and within-domain betweenness clustering, thereby propagating semantic label information from labeled samples across domains to unlabeled target data.

Clustering Semi-supervised Domain Adaptation +1

Inter-Domain Mixup for Semi-Supervised Domain Adaptation

no code implementations21 Jan 2024 Jichang Li, Guanbin Li, Yizhou Yu

However, existing SSDA work fails to make full use of label information from both source and target domains for feature alignment across domains, resulting in label mismatch in the label space during model testing.

Semi-supervised Domain Adaptation Unsupervised Domain Adaptation

Leveraging Frequency Domain Learning in 3D Vessel Segmentation

no code implementations11 Jan 2024 Xinyuan Wang, Chengwei Pan, Hongming Dai, Gangming Zhao, Jinpeng Li, Xiao Zhang, Yizhou Yu

In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models, which can reduce computational expenses while preserving global receptive fields within the network.


FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels

1 code implementation19 Dec 2023 Jichang Li, Guanbin Li, Hui Cheng, Zicheng Liao, Yizhou Yu

However, these prior methods do not learn noise filters by exploiting knowledge across all clients, leading to sub-optimal and inferior noise filtering performance and thus damaging training stability.

Federated Learning Learning with noisy labels +1

TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition

1 code implementation30 Oct 2023 Meng Lou, Hong-Yu Zhou, Sibei Yang, Yizhou Yu

Furthermore, when stacking token mixers that consist of convolution and self-attention to form a deep network, the static nature of convolution hinders the fusion of features previously generated by self-attention into convolution kernels.

Image Classification Object Detection +1

B-Spine: Learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation

no code implementations14 Oct 2023 Hao Wang, Qiang Song, Ruofeng Yin, Rui Ma, Yizhou Yu, Yi Chang

In this paper, we propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine and estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images.

Image-to-Image Translation

Activate and Reject: Towards Safe Domain Generalization under Category Shift

no code implementations ICCV 2023 Chaoqi Chen, Luyao Tang, Leitian Tao, Hong-Yu Zhou, Yue Huang, Xiaoguang Han, Yizhou Yu

Albeit the notable performance on in-domain test points, it is non-trivial for deep neural networks to attain satisfactory accuracy when deploying in the open world, where novel domains and object classes often occur.

Domain Generalization Image Classification +3

EMS: 3D Eyebrow Modeling from Single-view Images

no code implementations22 Sep 2023 Chenghong Li, Leyang Jin, Yujian Zheng, Yizhou Yu, Xiaoguang Han

Three modules are then carefully designed: RootFinder firstly localizes the fiber root positions which indicates where to grow; OriPredictor predicts an orientation field in the 3D space to guide the growing of fibers; FiberEnder is designed to determine when to stop the growth of each fiber.

Improved Distribution Matching for Dataset Condensation

2 code implementations CVPR 2023 Ganlong Zhao, Guanbin Li, Yipeng Qin, Yizhou Yu

In this paper, we propose a novel dataset condensation method based on distribution matching, which is more efficient and promising.

Dataset Condensation Model Optimization

SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling

no code implementations3 Jul 2023 Zhongjin Luo, Dong Du, Heming Zhu, Yizhou Yu, Hongbo Fu, Xiaoguang Han

User studies demonstrate the superiority of our system over existing modeling tools in terms of the ease to use and visual quality of results.

Exploration and Exploitation of Unlabeled Data for Open-Set Semi-Supervised Learning

no code implementations30 Jun 2023 Ganlong Zhao, Guanbin Li, Yipeng Qin, Jinjin Zhang, Zhenhua Chai, Xiaolin Wei, Liang Lin, Yizhou Yu

In this paper, we address a complex but practical scenario in semi-supervised learning (SSL) named open-set SSL, where unlabeled data contain both in-distribution (ID) and out-of-distribution (OOD) samples.

A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics

1 code implementation1 Jun 2023 Hong-Yu Zhou, Yizhou Yu, Chengdi Wang, Shu Zhang, Yuanxu Gao, Jia Pan, Jun Shao, Guangming Lu, Kang Zhang, Weimin Li

During the diagnostic process, clinicians leverage multimodal information, such as chief complaints, medical images, and laboratory-test results.

Representation Learning

Multi-Level Contrastive Learning for Dense Prediction Task

1 code implementation4 Apr 2023 Qiushan Guo, Yizhou Yu, Yi Jiang, Jiannan Wu, Zehuan Yuan, Ping Luo

We extend our pretext task to supervised pre-training, which achieves a similar performance to self-supervised learning.

Contrastive Learning Self-Supervised Learning

Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling

1 code implementation30 Jan 2023 Hong-Yu Zhou, Yunxiang Fu, Zhicheng Zhang, Cheng Bian, Yizhou Yu

Protein representation learning has primarily benefited from the remarkable development of language models (LMs).

Representation Learning

Advancing Radiograph Representation Learning with Masked Record Modeling

1 code implementation30 Jan 2023 Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, Yizhou Yu

Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed.

Representation Learning

Adaptive Context Selection for Polyp Segmentation

1 code implementation12 Jan 2023 Ruifei Zhang, Guanbin Li, Zhen Li, Shuguang Cui, Dahong Qian, Yizhou Yu

To tackle these issues, we propose an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM).

Decoder Segmentation

Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation

1 code implementation12 Jan 2023 Ruifei Zhang, Sishuo Liu, Yizhou Yu, Guanbin Li

Since the two tasks rely on similar feature information, the unlabeled data effectively enhances the representation of the network to the lesion regions and further improves the segmentation performance.

Image Segmentation Medical Image Segmentation +3

GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis

no code implementations11 Jan 2023 Hong-Yu Zhou, Chixiang Lu, Liansheng Wang, Yizhou Yu

Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks.

Contrastive Learning Lesion Segmentation +3

Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation

no code implementations6 Jan 2023 Gangming Zhao, Kongming Liang, Chengwei Pan, Fandong Zhang, Xianpeng Wu, Xinyang Hu, Yizhou Yu

To tackle the challenges caused by the sparsity and anisotropy of vessels, a higher percentage of graph nodes are distributed in areas that potentially contain vessels while a higher percentage of edges follow the orientation of potential nearbyvessels.


I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation

no code implementations3 Jan 2023 Haoyu Ma, Xiangru Lin, Yizhou Yu

This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation.

Segmentation Semantic Segmentation +1

PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis

1 code implementation2 Jan 2023 Hong-Yu Zhou, Chixiang Lu, Chaoqi Chen, Sibei Yang, Yizhou Yu

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views.

Brain Tumor Segmentation Organ Segmentation +3

Geometry-Aware Network for Domain Adaptive Semantic Segmentation

no code implementations2 Dec 2022 Yinghong Liao, Wending Zhou, Xu Yan, Shuguang Cui, Yizhou Yu, Zhen Li

Moreover, to improve the 2D classifier in the target domain, we perform domain-invariant geometric adaptation from source to target and unify the 2D semantic and 3D geometric segmentation results in two domains.

Depth Estimation Depth Prediction +4

RankDNN: Learning to Rank for Few-shot Learning

1 code implementation28 Nov 2022 Qianyu Guo, Hongtong Gong, Xujun Wei, Yanwei Fu, Weifeng Ge, Yizhou Yu, Wenqiang Zhang

This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification.

Few-Shot Learning Image Classification +4

UNet-2022: Exploring Dynamics in Non-isomorphic Architecture

no code implementations27 Oct 2022 Jiansen Guo, Hong-Yu Zhou, Liansheng Wang, Yizhou Yu

These phenomena indicate the potential of UNet-2022 to become the model of choice for medical image segmentation.

Image Segmentation Lesion Segmentation +4

Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization

no code implementations14 Oct 2022 Chaoqi Chen, Luyao Tang, Feng Liu, Gangming Zhao, Yue Huang, Yizhou Yu

Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one.

Domain Generalization Relational Reasoning

A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective

no code implementations27 Sep 2022 Chaoqi Chen, Yushuang Wu, Qiyuan Dai, Hong-Yu Zhou, Mutian Xu, Sibei Yang, Xiaoguang Han, Yizhou Yu

Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e. g.,} social network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.

Graph Representation Learning object-detection +3

Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis

no code implementations19 Aug 2022 Gangming Zhao, Quanlong Feng, Chaoqi Chen, Zhen Zhou, Yizhou Yu

On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95. 36\% and an AUC of 96. 54\%.


Neighborhood Collective Estimation for Noisy Label Identification and Correction

1 code implementation5 Aug 2022 Jichang Li, Guanbin Li, Feng Liu, Yizhou Yu

Specifically, our method is divided into two steps: 1) Neighborhood Collective Noise Verification to separate all training samples into a clean or noisy subset, 2) Neighborhood Collective Label Correction to relabel noisy samples, and then auxiliary techniques are used to assist further model optimization.

Learning with noisy labels Model Optimization

One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement

no code implementations31 Jul 2022 Zihao Yin, Ping Gong, Chunyu Wang, Yizhou Yu, Yizhou Wang

As an important upstream task for many medical applications, supervised landmark localization still requires non-negligible annotation costs to achieve desirable performance.

Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels

1 code implementation29 Jul 2022 Ganlong Zhao, Guanbin Li, Yipeng Qin, Feng Liu, Yizhou Yu

In this paper, we propose a two-stage clean samples identification method to address the aforementioned challenge.

Ranked #3 on Image Classification on Clothing1M (using extra training data)

Image Classification

Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance

1 code implementation1 Jul 2022 Chengwei Pan, Gangming Zhao, Junjie Fang, Baolian Qi, Jiaheng Liu, Chaowei Fang, Dingwen Zhang, Jinpeng Li, Yizhou Yu

Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption.

Attribute Relational Reasoning +1

Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection

no code implementations6 Jun 2022 Chaoqi Chen, Jiongcheng Li, Hong-Yu Zhou, Xiaoguang Han, Yue Huang, Xinghao Ding, Yizhou Yu

However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected.

Domain Adaptation Graph Attention +5

Domain Invariant Model with Graph Convolutional Network for Mammogram Classification

no code implementations21 Apr 2022 Churan Wang, Jing Li, Xinwei Sun, Fandong Zhang, Yizhou Yu, Yizhou Wang

To resolve this problem, we propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant disease-related features from multiple domains.


Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis

1 code implementation29 Mar 2022 Yunlong Zhang, Xin Lin, Yihong Zhuang, LiyanSun, Yue Huang, Xinghao Ding, Guisheng Wang, Lin Yang, Yizhou Yu

Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.

Generative Adversarial Network Image Enhancement +4

Scale-Equivalent Distillation for Semi-Supervised Object Detection

no code implementations CVPR 2022 Qiushan Guo, Yao Mu, Jianyu Chen, Tianqi Wang, Yizhou Yu, Ping Luo

Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.

Knowledge Distillation Object +3

Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning

1 code implementation CVPR 2022 Yangji He, Weihan Liang, Dongyang Zhao, Hong-Yu Zhou, Weifeng Ge, Yizhou Yu, Wenqiang Zhang

To improve data efficiency, we propose hierarchically cascaded transformers that exploit intrinsic image structures through spectral tokens pooling and optimize the learnable parameters through latent attribute surrogates.

Attribute Few-Shot Image Classification +2

PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds

no code implementations22 Feb 2022 Yushuang Wu, Zizheng Yan, Shengcai Cai, Guanbin Li, Yizhou Yu, Xiaoguang Han, Shuguang Cui

Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervised scheme with only sparse points annotated.

Representation Learning Weakly supervised Semantic Segmentation +1

BOAT: Bilateral Local Attention Vision Transformer

1 code implementation31 Jan 2022 Tan Yu, Gangming Zhao, Ping Li, Yizhou Yu

To improve efficiency, recent Vision Transformers adopt local self-attention mechanisms, where self-attention is computed within local windows.

Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit

no code implementations25 Jan 2022 Jianwei Xu, Ran Zhao, Yizhou Yu, Qingwei Zhang, Xianzhang Bian, Jun Wang, Zhizheng Ge, Dahong Qian

In order to solve these problems, our method combines the two-dimensional (2-D) CNN-based real-time object detector network with spatiotemporal information.

Specificity SSIM +1

Cross-Modality Deep Feature Learning for Brain Tumor Segmentation

no code implementations7 Jan 2022 Dingwen Zhang, Guohai Huang, Qiang Zhang, Jungong Han, Junwei Han, Yizhou Yu

Recent advances in machine learning and prevalence of digital medical images have opened up an opportunity to address the challenging brain tumor segmentation (BTS) task by using deep convolutional neural networks.

Brain Tumor Segmentation Segmentation +1

Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training

1 code implementation5 Jan 2022 Shu Zhang, Zihao Li, Hong-Yu Zhou, Jiechao Ma, Yizhou Yu

The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications.

Contrastive Learning Medical Object Detection

Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images

1 code implementation11 Oct 2021 Kongming Liang, Kai Han, Xiuli Li, Xiaoqing Cheng, Yiming Li, Yizhou Wang, Yizhou Yu

In this paper, we propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation.

Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition

no code implementations8 Oct 2021 Mingzhou Liu, Xinwei Sun, Fandong Zhang, Yizhou Yu, Yizhou Wang

Finally, to implement this contextual posterior, we introduce a Transformer that takes the object's information as a reference and locates correlated contextual factors.

Emotion Recognition Object +2

Scale-Invariant Teaching for Semi-Supervised Object Detection

no code implementations29 Sep 2021 Qiushan Guo, Yizhou Yu, Ping Luo

Furthermore, the limited annotations in semi-supervised learning scale up the challenges: large variance of object sizes and class imbalance (i. e., the extreme ratio between background and object), hindering the performance of prior arts.

Object object-detection +1

Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts

2 code implementations ICCV 2021 Hong-Yu Zhou, Chixiang Lu, Sibei Yang, Xiaoguang Han, Yizhou Yu

From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations.

Contrastive Learning Representation Learning +1

nnFormer: Interleaved Transformer for Volumetric Segmentation

2 code implementations7 Sep 2021 Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.

Image Segmentation Inductive Bias +3

Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System

no code implementations24 Aug 2021 Gang Yu, Zhongzhi Yu, Yemin Shi, Yingshuo Wang, Xiaoqing Liu, Zheming Li, Yonggen Zhao, Fenglei Sun, Yizhou Yu, Qiang Shu

The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage.

ME-PCN: Point Completion Conditioned on Mask Emptiness

1 code implementation ICCV 2021 Bingchen Gong, Yinyu Nie, Yiqun Lin, Xiaoguang Han, Yizhou Yu

Main-stream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details.

CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation

1 code implementation16 Aug 2021 Xinru Zhang, Chenghao Liu, Ni Ou, Xiangzhu Zeng, Xiaoliang Xiong, Yizhou Yu, Zhiwen Liu, Chuyang Ye

Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem.

Data Augmentation Lesion Segmentation +1

ConvNets vs. Transformers: Whose Visual Representations are More Transferable?

no code implementations11 Aug 2021 Hong-Yu Zhou, Chixiang Lu, Sibei Yang, Yizhou Yu

Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets.

Classification Depth Estimation +5

GraphFPN: Graph Feature Pyramid Network for Object Detection

2 code implementations ICCV 2021 Gangming Zhao, Weifeng Ge, Yizhou Yu

State-of-the-art methods for multi-scale feature learning focus on performing feature interactions across space and scales using neural networks with a fixed topology.

Object object-detection +1

Multi-scale Matching Networks for Semantic Correspondence

1 code implementation ICCV 2021 Dongyang Zhao, Ziyang Song, Zhenghao Ji, Gangming Zhao, Weifeng Ge, Yizhou Yu

We follow the coarse-to-fine matching strategy and build a top-down feature and matching enhancement scheme that is coupled with the multi-scale hierarchy of deep convolutional neural networks.

Computational Efficiency Semantic correspondence

GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images

1 code implementation14 Jul 2021 Baolian Qi, Gangming Zhao, Xin Wei, Changde Du, Chengwei Pan, Yizhou Yu, Jinpeng Li

To model the relationship, we propose the Graph Regularized Embedding Network (GREN), which leverages the intra-image and inter-image information to locate diseases on chest X-ray images.

Decision Making

Bottom-Up Shift and Reasoning for Referring Image Segmentation

1 code implementation CVPR 2021 Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, Yizhou Yu

In this paper, we tackle the challenge by jointly performing compositional visual reasoning and accurate segmentation in a single stage via the proposed novel Bottom-Up Shift (BUS) and Bidirectional Attentive Refinement (BIAR) modules.

Image Segmentation Segmentation +2

Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation

no code implementations31 May 2021 Chenxin Li, Wenao Ma, Liyan Sun, Xinghao Ding, Yue Huang, Guisheng Wang, Yizhou Yu

In this paper, to address the above issues, we propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels, and enhance the spatial activation in those areas for the sub-type vessels.


Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection

no code implementations21 May 2021 Yuhang Liu, Fandong Zhang, Chaoqi Chen, Siwen Wang, Yizhou Wang, Yizhou Yu

In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability.

Anatomy Decision Making +2

A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation

1 code implementation21 Apr 2021 Jie Lian, Jingyu Liu, Shu Zhang, Kai Gao, Xiaoqing Liu, Dingwen Zhang, Yizhou Yu

Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.

Instance Segmentation Object Detection +2

Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation

2 code implementations CVPR 2021 Jichang Li, Guanbin Li, Yemin Shi, Yizhou Yu

Pseudo labeling expands the number of ``labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning.

Clustering Domain Adaptation +1

Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation

no code implementations30 Mar 2021 Hong-Yu Zhou, Hualuo Liu, Shilei Cao, Dong Wei, Chixiang Lu, Yizhou Yu, Kai Ma, Yefeng Zheng

In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic.

One-Shot Segmentation Organ Segmentation +1

I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors

1 code implementation CVPR 2021 Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, Yizhou Yu

Motivated by this, we propose an Implicit Instance-Invariant Network (I3Net), which is tailored for adapting one-stage detectors and implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers.

Region Proposal

Scene-Intuitive Agent for Remote Embodied Visual Grounding

no code implementations CVPR 2021 Xiangru Lin, Guanbin Li, Yizhou Yu

Intuitively, we comprehend the semantics of the instruction to form an overview of where a bathroom is and what a blue towel is in mind; then, we navigate to the target location by consistently matching the bathroom appearance in mind with the current scene.

cross-modal alignment Navigate +2

Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection

no code implementations ICCV 2021 Chaoqi Chen, Jiongcheng Li, Zebiao Zheng, Yue Huang, Xinghao Ding, Yizhou Yu

Domain Adaptive Object Detection (DAOD) relieves the reliance on large-scale annotated data by transferring the knowledge learned from a labeled source domain to a new unlabeled target domain.

Domain Adaptation Graph Learning +2

Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices

1 code implementation16 Dec 2020 Shu Zhang, Jincheng Xu, Yu-Chun Chen, Jiechao Ma, Zihao Li, Yizhou Wang, Yizhou Yu

We demonstrate that with the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset (3. 48% absolute improvement in the sensitivity of FPs@0. 5), significantly surpassing the baseline method by up to 6. 06% (in MAP@0. 5) which adopts 2D convolution for 3D context modeling.

Computed Tomography (CT) Lesion Detection +2

Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction

no code implementations14 Dec 2020 Jiafa He, Chengwei Pan, Can Yang, Ming Zhang, Yang Wang, Xiaowei Zhou, Yizhou Yu

The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image.

Representation Learning

Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding

no code implementations10 Dec 2020 Liyan Sun, Chenxin Li, Xinghao Ding, Yue Huang, Guisheng Wang, Yizhou Yu

Motivated by the spatial consistency and regularity in medical images, we developed an efficient global correlation module to capture the correlation between a support and query image and incorporate it into the deep network called global correlation network.

Clustering Image Segmentation +2

Adaptive noise imitation for image denoising

no code implementations30 Nov 2020 Huangxing Lin, Yihong Zhuang, Yue Huang, Xinghao Ding, Yizhou Yu, Xiaoqing Liu, John Paisley

Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner.

Image Denoising

A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision

1 code implementation23 Oct 2020 Liyan Sun, Jianxiong Wu, Xinghao Ding, Yue Huang, Guisheng Wang, Yizhou Yu

We further proposed a localization branch realized via an aggregation of high-level features in a deep decoder to predict locations of organ and lesion, which enriches student segmentor with precise localization information.

Decoder Image Segmentation +3

Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation

no code implementations9 Oct 2020 Yicheng Wu, Chengwei Pan, Shuqi Wang, Ming Zhang, Yong Xia, Yizhou Yu

Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases.


Contralaterally Enhanced Networks for Thoracic Disease Detection

no code implementations9 Oct 2020 Gangming Zhao, Chaowei Fang, Guanbin Li, Licheng Jiao, Yizhou Yu

Aimed at improving the performance of existing detection methods, we propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals.

Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification

no code implementations30 Sep 2020 Chu-ran Wang, Jing Li, Fandong Zhang, Xinwei Sun, Hao Dong, Yizhou Yu, Yizhou Wang

Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations.

Classification counterfactual +1

Online Alternate Generator against Adversarial Attacks

no code implementations17 Sep 2020 Haofeng Li, Yirui Zeng, Guanbin Li, Liang Lin, Yizhou Yu

The field of computer vision has witnessed phenomenal progress in recent years partially due to the development of deep convolutional neural networks.

Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation

1 code implementation10 Jul 2020 Shen Wang, Kongming Liang, Yiming Li, Yizhou Yu, Yizhou Wang

Nevertheless, there are still great challenges with brain midline delineation, such as the largely deformed midline caused by the mass effect and the possible morphological failure that the predicted midline is not a connected curve.

ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities

1 code implementation17 Jun 2020 Jingyu Liu, Jie Lian, Yizhou Yu

Instance level detection of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images.

Classification General Classification

Graph-Structured Referring Expression Reasoning in The Wild

1 code implementation CVPR 2020 Sibei Yang, Guanbin Li, Yizhou Yu

The linguistic structure of a referring expression provides a layout of reasoning over the visual contents, and it is often crucial to align and jointly understand the image and the referring expression.

Referring Expression

Kernel Quantization for Efficient Network Compression

no code implementations11 Mar 2020 Zhongzhi Yu, Yemin Shi, Tiejun Huang, Yizhou Yu

Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio.


Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation

no code implementations27 Feb 2020 Shen Wang, Kongming Liang, Chengwei Pan, Chuyang Ye, Xiuli Li, Feng Liu, Yizhou Yu, Yizhou Wang

The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI).

Decision Making

When Relation Networks meet GANs: Relation GANs with Triplet Loss

1 code implementation24 Feb 2020 Runmin Wu, Kunyao Zhang, Lijun Wang, Yue Wang, Pingping Zhang, Huchuan Lu, Yizhou Yu

Though recent research has achieved remarkable progress in generating realistic images with generative adversarial networks (GANs), the lack of training stability is still a lingering concern of most GANs, especially on high-resolution inputs and complex datasets.

Conditional Image Generation Relation +2

Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread

no code implementations22 Jan 2020 Haofeng Li, Guanbin Li, BinBin Yang, Guanqi Chen, Liang Lin, Yizhou Yu

The proposed algorithm for the first time achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.

Image Classification Object +4

Self-Enhanced Convolutional Network for Facial Video Hallucination

no code implementations23 Nov 2019 Chaowei Fang, Guanbin Li, Xiaoguang Han, Yizhou Yu

It further recurrently exploits the reconstructed results and intermediate features of a sequence of preceding frames to improve the initial super-resolution of the current frame by modelling the coherence of structural facial features across frames.

Hallucination Video Super-Resolution

Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation

no code implementations23 Nov 2019 Chaowei Fang, Guanbin Li, Chengwei Pan, Yiming Li, Yizhou Yu

Recently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in computer aided diagnosis.

Organ Segmentation Pancreas Segmentation +1

Dynamic Graph Attention for Referring Expression Comprehension

no code implementations ICCV 2019 Sibei Yang, Guanbin Li, Yizhou Yu

In this paper, we explore the problem of referring expression comprehension from the perspective of language-driven visual reasoning, and propose a dynamic graph attention network to perform multi-step reasoning by modeling both the relationships among the objects in the image and the linguistic structure of the expression.

Graph Attention Referring Expression +2

Motion Guided Attention for Video Salient Object Detection

2 code implementations ICCV 2019 Haofeng Li, Guanqi Chen, Guanbin Li, Yizhou Yu

In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images.

Object object-detection +4

Relationship-Embedded Representation Learning for Grounding Referring Expressions

1 code implementation CVPR 2019 Sibei Yang, Guanbin Li, Yizhou Yu

Unfortunately, existing work on grounding referring expressions fails to accurately extract multi-order relationships from the referring expression and associate them with the objects and their related contexts in the image.

Referring Expression Representation Learning

ROSA: Robust Salient Object Detection against Adversarial Attacks

no code implementations9 May 2019 Haofeng Li, Guanbin Li, Yizhou Yu

To our knowledge, this paper is the first one that mounts successful adversarial attacks on salient object detection models and verifies that adversarial samples are effective on a wide range of existing methods.

Object object-detection +2

Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks

no code implementations27 Apr 2019 Xiang He, Sibei Yang, Guanbin Li?, Haofeng Li, Huiyou Chang, Yizhou Yu

In this paper, we discover that global spatial dependencies and global contextual information in a biomedical image can be exploited to defend against adversarial attacks.

Image Segmentation Lesion Segmentation +3

A Benchmark for Edge-Preserving Image Smoothing

1 code implementation2 Apr 2019 Feida Zhu, Zhetong Liang, Xixi Jia, Lei Zhang, Yizhou Yu

This benchmark includes an image dataset with groundtruth image smoothing results as well as baseline algorithms that can generate competitive edge-preserving smoothing results for a wide range of image contents.

image smoothing

Harvesting Visual Objects from Internet Images via Deep Learning Based Objectness Assessment

no code implementations1 Apr 2019 Kan Wu, Guanbin Li, Haofeng Li, Jianjun Zhang, Yizhou Yu

As a concrete example, a database of over 1. 2 million visual objects has been built using the proposed method, and has been successfully used in various data-driven image applications.

Image Generation Object +1

Multi-source weak supervision for saliency detection

1 code implementation CVPR 2019 Yu Zeng, Yunzhi Zhuge, Huchuan Lu, Lihe Zhang, Mingyang Qian, Yizhou Yu

To this end, we propose a unified framework to train saliency detection models with diverse weak supervision sources.

Caption Generation Saliency Prediction

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

12 code implementations28 Mar 2019 Huikai Wu, Junge Zhang, Kaiqi Huang, Kongming Liang, Yizhou Yu

Modern approaches for semantic segmentation usually employ dilated convolutions in the backbone to extract high-resolution feature maps, which brings heavy computation complexity and memory footprint.

Semantic Segmentation

Transductive Zero-Shot Learning with Visual Structure Constraint

1 code implementation NeurIPS 2019 Zi-Yu Wan, Dong-Dong Chen, Yan Li, Xingguang Yan, Junge Zhang, Yizhou Yu, Jing Liao

Based on the observation that visual features of test instances can be separated into different clusters, we propose a new visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (i. e. alleviate the above domain shift problem).

Zero-Shot Learning

Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning

no code implementations10 Dec 2018 Lingbo Liu, Guanbin Li, Yuan Xie, Yizhou Yu, Qing Wang, Liang Lin

In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network~(BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings.

Face Alignment Face Detection +2

Automatic Image Stylization Using Deep Fully Convolutional Networks

no code implementations27 Nov 2018 Feida Zhu, Yizhou Yu

Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent.

Image Stylization

Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical Rectification

1 code implementation18 Sep 2018 Weifeng Ge, Bingchen Gong, Yizhou Yu

With respect to a downsampled low resolution image, we model a high resolution image as a combination of two components, a deterministic component and a stochastic component.

Image Super-Resolution regression

CaricatureShop: Personalized and Photorealistic Caricature Sketching

no code implementations24 Jul 2018 Xiaoguang Han, Kangcheng Hou, Dong Du, Yuda Qiu, Yizhou Yu, Kun Zhou, Shuguang Cui

To construct the mapping between 2D sketches and a vertex-wise scaling field, a novel deep learning architecture is developed.

Caricature Face Model

ReCoNet: Real-time Coherent Video Style Transfer Network

8 code implementations3 Jul 2018 Chang Gao, Derun Gu, Fangjun Zhang, Yizhou Yu

Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos.

Semantic Segmentation Style Transfer +1

Moiré Photo Restoration Using Multiresolution Convolutional Neural Networks

1 code implementation8 May 2018 Yujing Sun, Yizhou Yu, Wenping Wang

While digital image quality is constantly being improved, taking high-quality photos of digital screens still remains challenging because the photos are often contaminated with moir\'{e} patterns, a result of the interference between the pixel grids of the camera sensor and the device screen.

Denoising Image Enhancement +1

Contrast-Oriented Deep Neural Networks for Salient Object Detection

no code implementations30 Mar 2018 Guanbin Li, Yizhou Yu

In this paper, we develop hybrid contrast-oriented deep neural networks to overcome the aforementioned limitations.

Object object-detection +2

Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields

1 code implementation ECCV 2018 Yongcheng Jing, Yang Liu, Yezhou Yang, Zunlei Feng, Yizhou Yu, DaCheng Tao, Mingli Song

In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control.

Style Transfer

Piecewise Flat Embedding for Image Segmentation

no code implementations ICCV 2015 Chaowei Fang, Zicheng Liao, Yizhou Yu

We introduce a new multi-dimensional nonlinear embedding -- Piecewise Flat Embedding (PFE) -- for image segmentation.

Clustering Contour Detection +3

Context-Aware Semantic Inpainting

no code implementations21 Dec 2017 Haofeng Li, Guanbin Li, Liang Lin, Yizhou Yu

Our proposed GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context.

Generative Adversarial Network Image Inpainting

High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

no code implementations ICCV 2017 Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, Yizhou Yu

Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network.


Folding membrane proteins by deep transfer learning

no code implementations28 Aug 2017 Sheng Wang, Zhen Li, Yizhou Yu, Jinbo Xu

Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling.

Transfer Learning

DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling

no code implementations7 Jun 2017 Xiaoguang Han, Chang Gao, Yizhou Yu

This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features.


Neural Style Transfer: A Review

8 code implementations11 May 2017 Yongcheng Jing, Yezhou Yang, Zunlei Feng, Jingwen Ye, Yizhou Yu, Mingli Song

We first propose a taxonomy of current algorithms in the field of NST.

Style Transfer

Predicting membrane protein contacts from non-membrane proteins by deep transfer learning

no code implementations24 Apr 2017 Zhen Li, Sheng Wang, Yizhou Yu, Jinbo Xu

Tested on 510 non-redundant MPs, our deep model (learned from only non-MPs) has top L/10 long-range contact prediction accuracy 0. 69, better than our deep model trained by only MPs (0. 63) and much better than a representative DCA method CCMpred (0. 47) and the CASP11 winner MetaPSICOV (0. 55).

Transfer Learning

Instance-Level Salient Object Segmentation

no code implementations CVPR 2017 Guanbin Li, Yuan Xie, Liang Lin, Yizhou Yu

Image saliency detection has recently witnessed rapid progress due to deep convolutional neural networks.

Ranked #17 on RGB Salient Object Detection on DUTS-TE (max F-measure metric)

Instance Segmentation Object +3

Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning

1 code implementation CVPR 2017 Weifeng Ge, Yizhou Yu

In this paper, we introduce a source-target selective joint fine-tuning scheme for improving the performance of deep learning tasks with insufficient training data.

General Classification Transfer Learning

Visual Saliency Detection Based on Multiscale Deep CNN Features

2 code implementations7 Sep 2016 Guanbin Li, Yizhou Yu

The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature.

Saliency Detection

Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks

1 code implementation25 Apr 2016 Zhen Li, Yizhou Yu

Inspired by the recent successes of deep neural networks, in this paper, we propose an end-to-end deep network that predicts protein secondary structures from integrated local and global contextual features.

Multi-Task Learning Protein Secondary Structure Prediction

LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling

1 code implementation18 Apr 2016 Zhen Li, Yukang Gan, Xiaodan Liang, Yizhou Yu, Hui Cheng, Liang Lin

Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts.

Scene Labeling

Deep Contrast Learning for Salient Object Detection

no code implementations CVPR 2016 Guanbin Li, Yizhou Yu

Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream.

Ranked #21 on RGB Salient Object Detection on DUTS-TE (max F-measure metric)

Object object-detection +2

Visual Saliency Based on Multiscale Deep Features

no code implementations CVPR 2015 Guanbin Li, Yizhou Yu

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision.

Image Segmentation Semantic Segmentation

Automatic Photo Adjustment Using Deep Neural Networks

1 code implementation24 Dec 2014 Zhicheng Yan, Hao Zhang, Baoyuan Wang, Sylvain Paris, Yizhou Yu

Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics.

Photo Retouching

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