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arXiv 2024年

作者： Yang, Shu Luo, Luyang Wang, Qiong Chen, Hao Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen Futian China

Existing state-of-the-art methods for surgical phase recognition either rely on the extraction of spatial-temporal features at a short-range temporal resolution or adopt the sequential extraction of the spatial and temporal features across the entire temporal resolution. However, these methods have limitations in modeling spatial-temporal dependency and addressing spatial-temporal redundancy: 1) These methods fail to effectively model spatial-temporal dependency, due to the lack of long-range information or joint spatial-temporal modeling. 2) These methods utilize dense spatial features across the entire temporal resolution, resulting in significant spatial-temporal redundancy. In this paper, we propose the Surgical Transformer (Surgformer) to address the issues of spatial-temporal modeling and redundancy in an end-to-end manner, which employs divided spatial-temporal attention and takes a limited set of sparse frames as input. Moreover, we propose a novel Hierarchical Temporal Attention (HTA) to capture both global and local information within varied temporal resolutions from a target frame-centric perspective. Distinct from conventional temporal attention that primarily emphasizes dense long-range similarity, HTA not only captures long-term information but also considers local latent consistency among informative frames. HTA then employs pyramid feature aggregation to effectively utilize temporal information across diverse temporal resolutions, thereby enhancing the overall temporal representation. Extensive experiments on two challenging benchmark datasets verify that our proposed Surgformer performs favorably against the state-of-the-art methods. The code is released at https://***/isyangshu/Surgformer. Copyright © 2024, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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Early Prediction of Student Performance with LSTM-Based Deep Neural Network

Early Prediction of Student Performance with LSTM-Based Deep...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Han Wan Mengying Li Zihao Zhong Xiaoyan Luo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Image Processing Center School of Astronautics Beihang University Beijing China

The shift to hybrid teaching during the COVID-19 pandemic brought about a real challenge to predict student performance and conduct timely interventions on at-risk students. This study proposed a deep neural network supporting the early prediction of student performance. Bidirectional LSTM, Global Average Pooling, and TIME MASK structure were utilized in the improved GritNet model. Subsequently, this study optimized the hyperparameters with the aid of the hill-climbing algorithm. Finally, on-campus data sets were used in experiments to evaluate the model's performance. Data were collected from a course that carried out multiple iterations from Fall 2021 to Fall 2022. In Fall 2022, the proposed model achieved a ROC-AUC value of 95.47% in the 8th week, while the baseline model only achieved 91.44% in the same week. Besides, the proposed model achieved a ROC-AUC value of 89.67% in the 4th week, which meant it had acceptable prediction performance in the early stage. The experimental findings demonstrated that the model was capable of predicting the academic performance of students in hybrid courses early on.

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Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology

arXiv

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arXiv 2024年

作者： Zhang, Yuqi Wang, Jiakai Peng, Taiying Zhang, Xiaoqian Yang, Yuancheng Tong, Chao School of Computer Science & Engineering State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Computer Science & Engineering Beihang University Beijing China School of Biological Science and Medical Engineering Beihang University Beijing China

Computational pathology (CPath) has significantly advanced the clinical practice of pathology. Despite the progress made, Multiple Instance Learning (MIL), a promising paradigm within CPath, continues to face challenges, particularly related to incomplete information utilization. Existing frameworks, such as those based on Convolutional Neural Networks (CNNs), attention, and selective scan space state sequential model (SSM), lack sufficient flexibility and scalability in fusing diverse features, and cannot effectively fuse diverse features. Additionally, current approaches do not adequately exploit order-related and order-independent features, resulting in suboptimal utilization of sequence information. To address these limitations, we propose a novel MIL framework called Mamba2MIL. Our framework utilizes the state space duality model (SSD) to model long sequences of patches of whole slide images (WSIs), which, combined with weighted feature selection, supports the fusion processing of more branching features and can be extended according to specific application needs. Moreover, we introduce a sequence transformation method tailored to varying WSI sizes, which enhances sequence-independent features while preserving local sequence information, thereby improving sequence information utilization. Extensive experiments demonstrate that Mamba2MIL surpasses state-of-the-art MIL methods. We conducted extensive experiments across multiple datasets, achieving improvements in nearly all performance metrics. Specifically, on the NSCLC dataset, Mamba2MIL achieves a binary tumor classification AUC of 0.9533 and an accuracy of 0.8794. On the BRACS dataset, it achieves a multiclass classification AUC of 0.7986 and an accuracy of 0.4981. The code is available at https://***/YuqiZhang-Buaa/Mamba2MIL. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Improving Multi-Person Pose Tracking with A Confidence Network

arXiv

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arXiv 2023年

作者： Fu, Zehua Zuo, Wenhang Hu, Zhenghui Liu, Qingjie Wang, Yunhong The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Hangzhou Innovation Institute Behang University Hangzhou310051 China

Human pose estimation and tracking are fundamental tasks for understanding human behaviors in videos. Existing top-down framework-based methods usually perform three-stage tasks: human detection, pose estimation and tracking. Although promising results have been achieved, these methods rely heavily on high-performance detectors and may fail to track persons who are occluded or miss-detected. To overcome these problems, in this paper, we develop a novel keypoint confidence network and a tracking pipeline to improve human detection and pose estimation in top-down approaches. Specifically, the keypoint confidence network is designed to determine whether each keypoint is occluded, and it is incorporated into the pose estimation module. In the tracking pipeline, we propose the Bbox-revision module to reduce missing detection and the ID-retrieve module to correct lost trajectories, improving the performance of the detection stage. Experimental results show that our approach is universal in human detection and pose estimation, achieving state-of-the-art performance on both PoseTrack 2017 and 2018 datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Combining Implicit-Explicit View Correlation for Light Field Semantic Segmentation

Combining Implicit-Explicit View Correlation for Light Field...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Ruixuan Cong Da Yang Rongshan Chen Sizhe Wang Zhenglong Cui Hao Sheng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beihang Hangzhou Innovation Institute Yuhang Faculty of Applied Sciences Macao Polytechnic University

Since light field simultaneously records spatial information and angular information of light rays, it is considered to be beneficial for many potential applications, and semantic segmentation is one of them. The regular variation of image information across views facilitates a comprehensive scene understanding. However, in the case of limited memory, the high-dimensional property of light field makes the problem more intractable than generic semantic segmentation, manifested in the difficulty of fully exploiting the relationships among views while maintaining contextual information in single view. In this paper, we propose a novel network called LF-IENet for light field semantic segmentation. It contains two different manners to mine complementary information from surrounding views to segment central view. One is implicit feature integration that leverages attention mechanism to compute inter-view and intra-view similarity to modulate features of central view. The other is explicit feature propagation that directly warps features of other views to central view under the guidance of disparity. They complement each other and jointly realize complementary information fusion across views in light field. The proposed method achieves outperforming performance on both real-world and synthetic light field datasets, demonstrating the effectiveness of this new architecture.

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Typical curve with G1 constraints for curve completion

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 283-294页

作者： Chuan He Gang Zhao Aizeng Wang Fei Hou Zhanchuan Cai Shaolin Li School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

This paper presents a novel algorithm for planar G1 interpolation using typical curves with monotonic *** G1 interpolation problem is converted into a system of nonlinear equations and sufficient conditions are provided to check whether there is a *** proposed algorithm was applied to a curve completion *** main advantages of the proposed method are its simple construction,compatibility with NURBS,and monotonic curvature.

关键词： Typical curves Monotonic curvature G1 interpolation Curve completion Euler spiral

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The Power of Age-based Reward in Fresh Information Acquisition

The Power of Age-based Reward in Fresh Information Acquisiti...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Zhiyuan Wang Qingkai Meng Shan Zhang Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Institute of Artificial Intelligence Beihang University Beijing China State Key Laboratory of Software Development Environment

Many Internet platforms collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer reward to incentivize users and compensate their costs incurred from information acquisition. In practice, the user cost (and its distribution) is hidden to the platform, thus it is challenging to determine the optimal reward. In this paper, we investigate how the platform dynamically rewards the users, aiming to jointly reduce the age of information (AoI) and the operational expenditure (OpEx). Due to the hidden cost distribution, this is an online non-convex learning problem with partial feedback. To overcome the challenge, we first design an age-based rewarding scheme, which decouples the OpEx from the unknown cost distribution and enables the platform to accurately control its OpEx. We then take advantage of the age-based rewarding scheme and propose an exponentially discretizing and learning (EDAL) policy for platform operation. We prove that the EDAL policy performs asymptotically as well as the optimal decision (derived based on the cost distribution). Simulation results show that the age-based rewarding scheme protects the platform’s OpEx from the influence of the user characteristics, and verify the asymptotic optimality of the EDAL policy.

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Learning Path Recommendation Based on Knowledge Tracing and Reinforcement Learning

Learning Path Recommendation Based on Knowledge Tracing and ...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Han Wan Baoliang Che Hongzhen Luo Xiaoyan Luo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Image Processing Center School of Astronautics Beihang University Beijing China

Adaptive and intelligent web-based educational systems are made to automate the adaptation of the system to the learners' behaviors and needs. Personalized e-learning platforms should make adaptive adjustments according to the individual students' interactions and their knowledge states (KS). This study proposes a more effective personalized learning path recommendation algorithm to promote the individualized development of students. First, the Dynamic key-Value Memory Network (DKVMN) is enhanced by integrating a learning behavior module, which is used to trace student knowledge states. Then, the proposed knowledge tracing model is used to simulate virtual students and train recommendation policy based on reinforcement learning (RL). The experimental results show that our personalized learning path recommendation algorithm increases the average knowledge state of students by 12.11% and 5.38% on two different data sets, respectively.

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Routing in LEO Satellite Networks: How Many Link-State Updates Do We Need?

Routing in LEO Satellite Networks: How Many Link-State Updat...

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Satellite Computing (Satellite), IEEE International Conference on

作者： Qian Shan Zhiyuan Wang Shan Zhang Qingkai Meng Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems State Key Laboratory of Software Development Environment Institute of Artificial Intelligence Beihang University Beijing China

Low earth orbit (LEO) satellite networks equipped with inter-satellite links (ISLs) will be an indispensable component of the future Internet. Routing protocol installed on LEO satellite networks plays a significant role on achieving efficient data delivery. LEO satellite networks exhibit two typical topology features, i.e., deterministic neighbor relationship and intermittent ISLs, which are different from terrestrial Internet. Hence the classic link-state routing protocols (e.g., OSPF) designed for terrestrial Internet are naturally unable to accommodate these topology features. In this paper, we present the design of localized fine-grained (LoFi) link-state routing protocol for LEO satellite networks. The key idea of LoFi is to reduce the dissemination range of coarse-grained link connectivity and incorporate fine-grained link-load awareness. Specifically, LoFi has two key parameters, i.e., state dissemination range and load variation threshold, which determine how far to disseminate link state update and how precise to detect the link load change, respectively. The two parameters allow the LEO satellite networks to flexibly achieve a desired tradeoff between good packet delivery performance and small control overhead. We evaluate the performance of LoFi via extensive packet-level experiments on OMNeT++ under Iridium constellation. Results show that LoFi improves the packet delivery performance (up to 94.4 %) and reduces the control overhead (up to 85.7 %) compared to state-of-the-art link-state protocol ELB.

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Optimizing Link-Identified Forwarding Framework in LEO Satellite Networks

Optimizing Link-Identified Forwarding Framework in LEO Satel...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Hefan Zhang Zhiyuan Wang Shan Zhang Qingkai Meng Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems State Key Laboratory of Software Development Environment Institute of Artificial Intelligence Beihang University Beijing China

Low earth orbit (LEO) satellite networks have the potential to provide low-latency communication with global coverage. To unleash this potential, it is crucial to achieve efficient data delivery. In this paper, we analyze the topology characteristics of LEO satellite networks, and propose a source-route-style forwarding framework. Specifically, we leverage the deterministic neighbor relationship and identify all the unidi-rectional inter-satellite links (ISLs). Moreover, our framework utilizes the in-packet bloom filter (BF) to store the source-route-style forwarding information. This way, the source satellite could encode multiple ISL identifiers into the BF, which actually specifies the forwarding path. The intermediate satellites only need to check whether the outgoing ISLs are encoded and forward packets accordingly. Due to false positives caused by BF, the more ISLs are encoded at a time, the more redundant forwardings emerge. To reduce forwarding overhead, we take into account segment encoding, allowing the source and intermediate satellites to encode part of ISLs towards the destination. Overall, segment encoding seeks the right balance between forwarding overhead and encoding delay. We characterize a wide range of segment encoding policy in a unified framework, and derive the expected forwarding overhead in a closed-form. The segment encoding design is formulated as a binary non-linear programming, which is NP-hard. To overcome the challenge, we leverage its decomposable structure, and propose an efficient algorithm to solve it optimally. Finally, we validate our analytical results via packet-level experiments. Results also show that our proposed segment encoding policy significantly reduces the queuing delay compared to source encoding.

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