Author name cluster

Bing Yang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers

2 author rows

AAAI Conference 2026 Conference Paper

KeenKT: Knowledge Mastery-State Disambiguation for Knowledge Tracing

Zhifei Li
Lifan Chen
Jiali Yi
Xiaoju Hou
Yue Zhao
Wenxin Huang
Miao Zhang
Kui Xiao

Knowledge Tracing (KT) aims to dynamically model a student’s mastery of knowledge concepts based on their historical learning interactions. Most current methods rely on single-point estimates, which cannot distinguish true ability from outburst or carelessness, creating ambiguity in judging mastery. To address this issue, we propose a Knowledge Mastery-State Disambiguation for Knowledge Tracing model (KeenKT), which represents a student’s knowledge state at each interaction using a Normal-Inverse-Gaussian (NIG) distribution, thereby capturing the fluctuations in student learning behaviors. Furthermore, we design an NIG-distance-based attention mechanism to model the dynamic evolution of the knowledge state. In addition, we introduce a diffusion-based denoising reconstruction loss and a distributional contrastive learning loss to enhance the model’s robustness. Extensive experiments on six public datasets demonstrate that KeenKT outperforms state-of-the-art KT models in terms of prediction accuracy and sensitivity to behavioral fluctuations. The proposed method yields the maximum AUC improvement of 5.85% and the maximum ACC improvement of 6.89%.

PDF Details DOI

AAAI Conference 2026 Conference Paper

MacVQA: Adaptive Memory Allocation and Global Noise Filtering for Continual Visual Question Answering

Zhifei Li
Yiran Wang
Chenyi Xiong
Yujing Xia
Xiaoju Hou
Yue Zhao
Miao Zhang
Kui Xiao

Visual Question Answering (VQA) requires models to reason over multimodal information, combining visual and textual data. With the development of continual learning, significant progress has been made in retaining knowledge and adapting to new information in the VQA domain. However, current methods often struggle with balancing knowledge retention, adaptation, and robust feature representation. To address these challenges, we propose a novel framework with adaptive memory allocation and global noise filtering called MacVQA for visual question answering. MacVQA fuses visual and question information while filtering noise to ensure robust representations, and employs prototype-based memory allocation to optimize feature quality and memory usage. These designs enable MacVQA to balance knowledge acquisition, retention, and compositional generalization in continual VQA learning. Experiments on ten continual VQA tasks show that MacVQA outperforms existing baselines, achieving 43.38% average accuracy and 2.32% average forgetting on standard tasks, and 42.53% average accuracy and 3.60% average forgetting on novel composition tasks.

PDF Details DOI

AAAI Conference 2026 Conference Paper

MyGram: Modality-aware Graph Transformer with Global Distribution for Multi-modal Entity Alignment

Zhifei Li
Ziyue Qin
Xiangyu Luo
Xiaoju Hou
Yue Zhao
Miao Zhang
Zhifang Huang
Kui Xiao

Multi-modal entity alignment aims to identify equivalent entities between two multi-modal Knowledge graphs by integrating multi-modal data, such as images and text, to enrich the semantic representations of entities. However, existing methods may overlook the structural contextual information within each modality, making them vulnerable to interference from shallow features. To address these challenges, we propose MyGram, a \textbf{m}odalit\textbf{y}-aware \textbf{gra}ph transformer with global distribution for \textbf{m}ulti-modal entity alignment. Specifically, we develop a modality diffusion learning module to capture deep structural contextual information within modalities and enable fine-grained multi-modal fusion. In addition, we introduce a Gram Loss that acts as a regularization constraint by minimizing the volume of a 4-dimensional parallelotope formed by multi-modal features, thereby achieving global distribution consistency across modalities. We conduct experiments on five public datasets. Results show that MyGram outperforms baseline models, achieving a maximum improvement of 4.8\% in Hits@1 on FBDB15K, 9.9\% on FBYG15K, and 4.3\% on DBP15K.

PDF Details DOI

JBHI Journal 2025 Journal Article

Score Prior Guided Iterative Solver for Speckles Removal in Optical Coherent Tomography Images

Sanqian Li
Risa Higashita
Huazhu Fu
Bing Yang
Jiang Liu

Optical coherence tomography (OCT) is a widely used non-invasive imaging modality for ophthalmic diagnosis. However, the inherent speckle noise becomes the leading cause of OCT image quality, and efficient speckle removal algorithms can improve image readability and benefit automated clinical analysis. As an ill-posed inverse problem, it is of utmost importance for speckle removal to learn suitable priors. In this work, we develop a score prior guided iterative solver (SPIS) with logarithmic space to remove speckles in OCT images. Specifically, we model the posterior distribution of raw OCT images as a data consistency term and transform the speckle removal from a nonlinear into a linear inverse problem in the logarithmic domain. Subsequently, the learned prior distribution through the score function from the diffusion model is utilized as a constraint for the data consistency term into the linear inverse optimization, resulting in an iterative speckle removal procedure that alternates between the score prior predictor and the subsequent non-expansive data consistency corrector. Experimental results on the private and public OCT datasets demonstrate that the proposed SPIS has an excellent performance in speckle removal and out-of-distribution (OOD) generalization. Further downstream automatic analysis on the OCT images verifies that the proposed SPIS can benefit clinical applications.

Details DOI

JBHI Journal 2024 Journal Article

Label-Decoupled Medical Image Segmentation With Spatial-Channel Graph Convolution and Dual Attention Enhancement

Qingting Jiang
Hailiang Ye
Bing Yang
Feilong Cao

Deep learning-based methods have been widely used in medical image segmentation recently. However, existing works are usually difficult to simultaneously capture global long-range information from images and topological correlations among feature maps. Further, medical images often suffer from blurred target edges. Accordingly, this paper proposes a novel medical image segmentation framework named a label-decoupled network with spatial-channel graph convolution and dual attention enhancement mechanism (LADENet for short). It constructs learnable adjacency matrices and utilizes graph convolutions to effectively capture global long-range information on spatial locations and topological dependencies between different channels in an image. Then a label-decoupled strategy based on distance transformation is introduced to decouple an original segmentation label into a body label and an edge label for supervising the body branch and edge branch. Again, a dual attention enhancement mechanism, designing a body attention block in the body branch and an edge attention block in the edge branch, is built to promote the learning ability of spatial region and boundary features. Besides, a feature interactor is devised to fully consider the information interaction between the body and edge branches to improve segmentation performance. Experiments on benchmark datasets reveal the superiority of LADENet compared to state-of-the-art approaches.

Details DOI

NeurIPS Conference 2024 Conference Paper

RealMAN: A Real-Recorded and Annotated Microphone Array Dataset for Dynamic Speech Enhancement and Localization

Bing Yang
Changsheng Quan
Yabo Wang
Pengyu Wang
Yujie Yang
Ying Fang
Nian Shao
Hui Bu

The training of deep learning-based multichannel speech enhancement and source localization systems relies heavily on the simulation of room impulse response and multichannel diffuse noise, due to the lack of large-scale real-recorded datasets. However, the acoustic mismatch between simulated and real-world data could degrade the model performance when applying in real-world scenarios. To bridge this simulation-to-real gap, this paper presents a new relatively large-scale Real-recorded and annotated Microphone Array speech&Noise (RealMAN) dataset. The proposed dataset is valuable in two aspects: 1) benchmarking speech enhancement and localization algorithms in real scenarios; 2) offering a substantial amount of real-world training data for potentially improving the performance of real-world applications. Specifically, a 32-channel array with high-fidelity microphones is used for recording. A loudspeaker is used for playing source speech signals (about 35 hours of Mandarin speech). A total of 83. 7 hours of speech signals (about 48. 3 hours for static speaker and 35. 4 hours for moving speaker) are recorded in 32 different scenes, and 144. 5 hours of background noise are recorded in 31 different scenes. Both speech and noise recording scenes cover various common indoor, outdoor, semi-outdoor and transportation environments, which enables the training of general-purpose speech enhancement and source localization networks. To obtain the task-specific annotations, speaker location is annotated with an omni-directional fisheye camera by automatically detecting the loudspeaker. The direct-path signal is set as the target clean speech for speech enhancement, which is obtained by filtering the source speech signal with an estimated direct-path propagation filter. Baseline experiments demonstrate that i) compared to using simulated data, the proposed dataset is indeed able to train better speech enhancement and source localization networks; ii) using various sub-arrays of the proposed 32-channel microphone array can successfully train variable-array networks that can be directly used to unseen arrays.

PDF Details DOI

AAAI Conference 2021 Conference Paper

Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition

Zhan Chen
Sicheng Li
Bing Yang
Qinghan Li
Hong Liu

Graph convolutional networks have been widely used for skeleton-based action recognition due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a local operation, it can only utilize the short-range joint dependencies and short-term trajectory but fails to directly model the distant joints relations and long-range temporal information that are vital to distinguishing various actions. To solve this problem, we present a multi-scale spatial graph convolution (MS-GC) module and a multi-scale temporal graph convolution (MT-GC) module to enrich the receptive field of the model in spatial and temporal dimensions. Concretely, the MS-GC and MT-GC modules decompose the corresponding local graph convolution into a set of subgraph convolution, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-graph convolutions, and each node could complete multiple spatial and temporal aggregations with its neighborhoods. The final equivalent receptive field is accordingly enlarged, which is capable of capturing both short- and long-range dependencies in spatial and temporal domains. By coupling these two modules as a basic block, we further propose a multi-scale spatial temporal graph convolutional network (MST-GCN), which stacks multiple blocks to learn effective motion representations for action recognition. The proposed MST-GCN achieves remarkable performance on three challenging benchmark datasets, NTU RGB+D, NTU-120 RGB+D and Kinetics-Skeleton, for skeleton-based action recognition.

PDF Details

EAAI Journal 2014 Journal Article

A highly accurate dense approach for homography estimation using modified differential evolution

Lai Kang
Lingda Wu
Yingmei Wei
Bing Yang
Hanchen Song

The homography corresponds to a 3×3 matrix which transfers image points between two images of a planar scene or two images captured by cameras under purely rotational motion. Homography estimation is crucial to many computer vision tasks involving multi-view geometry. Unlike most existing algorithms using sparse information extracted from images, this paper proposes a dense homography estimation method taking into account both the color and gradient information of the whole image. In particular, homography estimation is recast as a two-objective optimization problem, which is solved within a global optimization procedure guided by sparse control points (SCPs) based on modified differential evolution (DE). In order to improve the computational efficiency, two strategies namely pre-evaluation and coarse-to-fine (C2F) search are integrated into the proposed framework. The experimental results on synthetically rendered images and real images demonstrate that the new method can consistently improve the accuracy of homography estimation compared with two most successful feature based algorithms, and the incorporated acceleration strategies are able to speed up the minimization procedure by about 15 times. The applicability of our proposed method is further demonstrated in two real-world applications, namely occlusion removal and image mosaicing.

Details DOI

ICRA Conference 2001 Conference Paper

New Control Strategy of Variable Reluctance Direct Drive Motor for Robotic Applications

Bing Yang
Dianguo Xu 0001
Zongpei Wang

Dual Stator Variable Reluctance Motors (VRM) are electrically commutated ac machines and can be used as direct drive actuators, but inherent nonlinearity of VRM results in difficulties, when smooth and quiet operation are required. In this paper, a high performance control system for VRM is presented. The whole control algorithms consist of four closed-loop controllers: position, velocity, torque and current controller. In the torque controller, the torque ripple is reduced via the torque-sharing function and current PWM control, and the command current is easily obtained from the shared torque command via the proposed nonlinear model of the motor. The velocity controller is a combination of fuzzy and proportional-integral algorithm. The lead angle algorithm is also introduced to improve the performance of system in high speed. The position controller is intended for velocity trajectory tracking. The experimental results illustrate the effectiveness of the control system.

Details