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Temporal-Interim Pose Synthesis and Distillation for Dynamic Human Pose Estimation
Renjie Zhang;Di Lin;Xin Wang;Ruonan Liu;Bin Sheng;George Baciu;C. L. Philip Chen;Ping Li
IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Early Access Article |
In the task of dynamic human pose estimation (dynamic HPE), the temporal relationships between human body parts should be captured comprehensively to understand the dynamic human motions, where the correlated motion information eventually helps to recognize body parts. The popular methods are successful in terms of utilizing long-term motion information captured by low-speed cameras. Yet they negl...Show More
SGG-Nets: Generic Rotation-Invariant Plugin Networks for Point Cloud Analysis
Jian Zhu;Jianrong Yan;Jiebin Huang;Yongwei Nie;Bin Sheng;Tong-Yee Lee
IEEE Transactions on Multimedia
Year: 2025 | Early Access Article |
Rotation invariance is a crucial requirement for the analysis of 3D point clouds. However, current methods often achieve rotation invariance by employing specific network designs. These networks, though perform well on rotation-aware tasks, is inferior in general tasks such as classification and segmentation. On the other hand, many powerful point processing networks, such as PointNet++, DGCNN, et...Show More
Identity and Modality Attributes Driven Multimodal Fusion Networks for Emotion Recognition in Conversations
IEEE Transactions on Multimedia
Year: 2025 | Early Access Article |
Emotion recognition in conversations (ERC) is a crucial aspect of human-computer interaction and plays an important role in various domains, including healthcare, entertainment, and education. Since the conversation data in the form of multimodal sequences is well suited to be constructed into graphs, the methods based on graph convolutional network (GCN) show incomparable advantages. However, exi...Show More
MHFNet: A Multimodal Hybrid-embedding Fusion Network for Automatic Sleep Staging
Ruhan Liu;Jiajia Li;Yang Wen;Xian Huang;Bin Sheng;David Dagan Feng;Ping Zhang
IEEE Journal of Biomedical and Health Informatics
Year: 2025 | Early Access Article |
Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global temporal information. Second, the correlation for special waves in different signals is rarely used in sleep staging modeling. Third, the logic of scoring ru...Show More
S2P-Matching: Self-Supervised Patch-Based Matching Using Transformer for Capsule Endoscopic Images Stitching
Feng Lu;Dao Zhou;Haoyang Chen;Shuai Liu;Xianliang Ling;Lei Zhu;Tingting Gong;Bin Sheng;Xiaofei Liao;Hai Jin;Ping Li;David Dagan Feng
The Magnetically Controlled Capsule Endoscopy (MCCE) has a limited shooting range, resulting in capturing numerous fragmented images and an inability to precisely locate and examine the region of interest (ROI) as traditional endoscopy can. Addressing this issue, image stitching around the ROI can be employed to aid in the diagnosis of gastrointestinal (GI) tract conditions. However, MCCE images p...Show More
MSCE-LT: Multi-Label Supervised Contrastive Enhancement for Long-Tailed Retinal Diseases Recognition
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2024 | Conference Paper |
Retinal diseases are leading causes of blindness globally. In real-world clinical practice, a patient may suffer from multiple retinal diseases, and these diseases are often under a long-tailed distribution, which poses significant challenges for accurate diagnosis. In this work, we propose a novel contrastive learning(CL)-based framework for multi-label retinal disease recognition. It consists of...Show More
Self-Paced Co-Training and Foundation Model for Semi-Supervised Medical Image Segmentation
2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Semi-supervised learning is an effective approach for image segmentation, especially in medical images where segmentation labels are scarce and require expertise. Currently, there is still much room for improvement in these existing semi-supervised methods. In this paper, we propose a semi-supervised learning method for image segmentation with self-paced learning and foundation model. We first syn...Show More
Learning Motion-Guided Multi-Scale Memory Features for Video Shadow Detection
Junhao Lin;Jiaxing Shen;Xin Yang;Huazhu Fu;Qing Zhang;Ping Li;Bin Sheng;Liansheng Wang;Lei Zhu
Natural images often contain multiple shadow regions, and existing video shadow detection methods tend to fail in fully identifying all shadow regions, since they mainly learned temporal features at single-scale and single memory. In this work, we develop a novel convolutional neural network (CNN) to learn motion-guided multi-scale memory features to obtain multi-scale temporal information based o...Show More
Multi-Label Chest X-Ray Image Classification With Single Positive Labels
Jiayin Xiao;Si Li;Tongxu Lin;Jian Zhu;Xiaochen Yuan;David Dagan Feng;Bin Sheng
Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduce a weakly supervised learning problem called Single Positive Multi-label Learning (SPML) into CXR images classification (abbreviated as SPML-CXR), in...Show More
Multiple Level of Details Neural Implicit Surface Representation for Unconstrained Viewpoint Rendering
2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
Year: 2024 | Conference Paper |
To mitigate artifacts and performance degradation in neural implicit representation visualization for unconstrained viewpoint rendering, we propose a feature voxel grid-based neural representation architecture. This approach flexibly encodes the implicit surface with multiple levels of detail, facilitating high-quality rendering with dynamic switching between detail levels. Additionally, we implem...Show More
MA-MFCNet: Mixed Attention-Based Multi-Scale Feature Calibration Network for Image Dehazing
High-quality clear images are the basis for advanced vision tasks such as target detection and semantic segmentation. This paper proposes an image dehazing algorithm named mixed attention-based multi-scale feature calibration network, aiming at solving the problem of uneven haze distribution in low-quality fuzzy images acquired in foggy environments, which is difficult to remove effectively. Our a...Show More
Clustering Environment Aware Learning for Active Domain Adaptation
Jian Zhu;Xinyu Chen;Qintai Hu;Yutang Xiao;Boyu Wang;Bin Sheng;C. L. Philip Chen
Despite the significant progress in unsupervised domain adaptation (UDA), the performance of UDA methods is still far inferior to that of the fully supervised ones. In practical scenarios, it is usually feasible to acquire labels on a small portion of the target data through active learning (AL), which aims to train an effective model with as few queried instances as possible. However, due to the ...Show More
Dual Branch Multi-Level Semantic Learning for Few-Shot Segmentation
Yadang Chen;Ren Jiang;Yuhui Zheng;Bin Sheng;Zhi-Xin Yang;Enhua Wu
Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Although progress has been made recently by combining prototype-based metric learning, existing methods still face two main challenges. First, various intra-class objects between the support and query images or semantically similar inter-class objects can seriou...Show More
Efficient Binocular Rendering of Volumetric Density Fields With Coupled Adaptive Cube-Map Ray Marching for Virtual Reality
Creating visualizations of multiple volumetric density fields is demanding in virtual reality (VR) applications, which often include divergent volumetric density distributions mixed with geometric models and physics-based simulations. Real-time rendering of such complex environments poses significant challenges for rendering quality and performance. This article presents a novel scheme for efficie...Show More
FastAL: Fast Evaluation Module for Efficient Dynamic Deep Active Learning Using Broad Learning System
State-of-the-art Active Learning (AL) methods often encounter challenges associated with a hysteretic learning process and an expensive data sampling mechanism. The former implies that data selection in the ( $i+1$ )-th round is solely based on the learned model’s results in the $i$ -th round. The latter involves using model inference to calculate data value (e.g., uncertainty estimation based on...Show More
DSMT-Net: Dual Self-Supervised Multi-Operator Transformation for Multi-Source Endoscopic Ultrasound Diagnosis
Jiajia Li;Pingping Zhang;Teng Wang;Lei Zhu;Ruhan Liu;Xia Yang;Kaixuan Wang;Dinggang Shen;Bin Sheng
Pancreatic cancer has the worst prognosis of all cancers. The clinical application of endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep learning for the classification of EUS images have been hindered by inter-grader variability and labeling capability. One of the key reasons for these difficulties is that EUS images are obtained from multiple sources with varyin...Show More
Unsupervised Fusion Feature Matching for Data Bias in Uncertainty Active Learning
Wei Huang;Shuzhou Sun;Xiao Lin;Ping Li;Lei Zhu;Jihong Wang;C. L. Philip Chen;Bin Sheng
Active learning (AL) aims to sample the most valuable data for model improvement from the unlabeled pool. Traditional works, especially uncertainty-based methods, are prone to suffer from a data bias issue, which means that selected data cannot cover the entire unlabeled pool well. Although there have been lots of literature works focusing on this issue recently, they mainly benefit from the huge ...Show More
SparseVoxNet: 3-D Object Recognition With Sparsely Aggregation of 3-D Dense Blocks
Ahmad Karambakhsh;Bin Sheng;Ping Li;Huating Li;Jinman Kim;Younhyun Jung;C. L. Philip Chen
Automatic recognition of 3-D objects in a 3-D model by convolutional neural network (CNN) methods has been successfully applied to various tasks, e.g., robotics and augmented reality. Three-dimensional object recognition is mainly performed by analyzing the object using multi-view images, depth images, graphs, or volumetric data. In some cases, using volumetric data provides the most promising res...Show More
MNGNAS: Distilling Adaptive Combination of Multiple Searched Networks for One-Shot Neural Architecture Search
Zhihua Chen;Guhao Qiu;Ping Li;Lei Zhu;Xiaokang Yang;Bin Sheng
Recently neural architecture (NAS) search has attracted great interest in academia and industry. It remains a challenging problem due to the huge search space and computational costs. Recent studies in NAS mainly focused on the usage of weight sharing to train a SuperNet once. However, the corresponding branch of each subnetwork is not guaranteed to be fully trained. It may not only incur huge com...Show More
TMM-Nets: Transferred Multi- to Mono-Modal Generation for Lupus Retinopathy Diagnosis
Ruhan Liu;Tianqin Wang;Huating Li;Ping Zhang;Jing Li;Xiaokang Yang;Dinggang Shen;Bin Sheng
Rare diseases, which are severely underrepresented in basic and clinical research, can particularly benefit from machine learning techniques. However, current learning-based approaches usually focus on either mono-modal image data or matched multi-modal data, whereas the diagnosis of rare diseases necessitates the aggregation of unstructured and unmatched multi-modal image data due to their rare a...Show More
We introduce a new photographing guidance (PhotoHelper) for amateur photographers to enhance their portrait photo quality using deep feature retrieval and fusion. In our model, we comprehensively integrate empirical aesthetic rules, traditional machine learning algorithms and deep neural networks to extract different kinds of features in both color and space aspects. With these features, we build ...Show More
EAPT: Efficient Attention Pyramid Transformer for Image Processing
Xiao Lin;Shuzhou Sun;Wei Huang;Bin Sheng;Ping Li;David Dagan Feng
Recent transformer-based models, especially patch-based methods, have shown huge potentiality in vision tasks. However, the split fixed-size patches divide the input features into the same size patches, which ignores the fact that vision elements are often various and thus may destroy the semantic information. Also, the vanilla patch-based transformer cannot guarantee the information communication...Show More
CDX-NET: Cross-Domain Multi-Feature Fusion Modeling Via Deep Neural Networks for Multivariate Time Series Forecasting in AIOps
Jiajia Li;Ling Dai;Feng Tan;Hui Shen;Zikai Wang;Bin Sheng;Pengwei Hu
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
In the application of Artificial Intelligence for IT Operations (AIOps), monitoring data are usually modeled as MTS (Multivariate Time Series). The prediction of MTS has been widely studied and various models, including statistic algorithms and deep learning networks, have been proposed, which attempt to capture the multi-dimensional and non-linear features. To this end, this paper focuses on one ...Show More
CDX-NET: Cross-Domain Multi-Feature Fusion Modeling Via Deep Neural Networks for Multivariate Time Series Forecasting in AIOps
Jiajia Li;Ling Dai;Feng Tan;Hui Shen;Zikai Wang;Bin Sheng;Pengwei Hu
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Mixed-Weight Neural Bagging for Detecting $m^6A$ Modifications in SARS-CoV-2 RNA Sequencing
Ruhan Liu;Liang Ou;Bin Sheng;Pei Hao;Ping Li;Xiaokang Yang;Guangtao Xue;Lei Zhu;Yuyang Luo;Ping Zhang;Po Yang;Huating Li;David Dagan Feng
Objective: The m6A modification is the most common ribonucleic acid (RNA) modification, playing a role in prompting the virus's gene mutation and protein structure changes in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Nanopore single-molecule direct RNA sequencing (DRS) provides data support for RNA modification detection, which can preserve the potential $m^6A$ signature co...Show More
ECSU-Net: An Embedded Clustering Sliced U-Net Coupled With Fusing Strategy for Efficient Intervertebral Disc Segmentation and Classification
Anam Nazir;Muhammad Nadeem Cheema;Bin Sheng;Ping Li;Huating Li;Guangtao Xue;Jing Qin;Jinman Kim;David Dagan Feng
Automatic vertebra segmentation from computed tomography (CT) image is the very first and a decisive stage in vertebra analysis for computer-based spinal diagnosis and therapy support system. However, automatic segmentation of vertebra remains challenging due to several reasons, including anatomic complexity of spine, unclear boundaries of the vertebrae associated with spongy and soft bones. Based...Show More
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