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学者姓名：郭文忠
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Abstract ：
Few-shot object detection achieves rapid detection of novel-class objects by training detectors with a minimal number of novel-class annotated instances. Transfer learning-based few-shot object detection methods have shown better performance compared to other methods such as meta-learning. However, when training with base-class data, the model may gradually bias towards learning the characteristics of each category in the base-class data, which could result in a decrease in learning ability during fine-tuning on novel classes, and further overfitting due to data scarcity. In this paper, we first find that the generalization performance of the base-class model has a significant impact on novel class detection performance and proposes a generalization feature extraction network framework to address this issue. This framework perturbs the base model during training to encourage it to learn generalization features and solves the impact of changes in object shape and size on overall detection performance, improving the generalization performance of the base model. Additionally, we propose a feature-level data augmentation method based on self-distillation to further enhance the overall generalization ability of the model. Our method achieves state-of-the-art results on both the COCO and PASCAL VOC datasets, with a 6.94% improvement on the PASCAL VOC 10-shot dataset. IEEE
Keyword ：
Adaptation models Adaptation models Computational modeling Computational modeling data augmentation data augmentation Data models Data models Feature extraction Feature extraction few-shot learning few-shot learning object detection object detection Object detection Object detection self-distillation self-distillation Shape Shape Training Training Transfer learning Transfer learning
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| GB/T 7714 | Ke, X. , Chen, Q. , Liu, H. et al. GFENet: Generalization Feature Extraction Network for Few-Shot Object Detection [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2024 : 1-1 . |
| MLA | Ke, X. et al. "GFENet: Generalization Feature Extraction Network for Few-Shot Object Detection" . | IEEE Transactions on Circuits and Systems for Video Technology (2024) : 1-1 . |
| APA | Ke, X. , Chen, Q. , Liu, H. , Guo, W. . GFENet: Generalization Feature Extraction Network for Few-Shot Object Detection . | IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 1-1 . |
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Fully programmable valve array (FPVA) biochips have emerged as a promising alternative for traditional application-specific microfluidic platforms thanks to their advantages in terms of flexibility and reconfigurability. By regularly deploying microvalves along vertical and horizontal flow channels, microfluidic modules with different sizes and shapes can be constructed dynamically on the chip, thereby enabling the automatic execution of various assay procedures in biology and biochemistry. The above advantages, however, result largely from the large-scale integration of valves as well as accurate control of their switchings, leading to very complicated control-logic design of such chips. In this article, we propose an reinforcement learning (RL)-based synthesis flow for the control-logic design of fully programmable valve array (FPVA) biochips, taking multichannel switching and control-cost minimization into consideration simultaneously. By employing a double deep Q-network (DDQN) and two Boolean-logic simplification techniques, control logics with both high-switching efficiency and low-fabrication cost can be constructed automatically. Furthermore, the solution space of multichannel-switching combinations is reduced to improve the search efficiency of the proposed method. Experimental results on multiple benchmarks demonstrate that the proposed synthesis flow leads to better-design solutions compared with the state-of-the-art techniques.
Keyword ：
Boolean logic simplification Boolean logic simplification Cells (biology) Cells (biology) control logic control logic Control systems Control systems fully programmable valve array (FPVA) fully programmable valve array (FPVA) Image color analysis Image color analysis microfluidics microfluidics Mixers Mixers Multiplexing Multiplexing reinforcement learning (RL) reinforcement learning (RL) Switches Switches Valves Valves
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| GB/T 7714 | Huang, Xing , Cai, Huayang , Guo, Wenzhong et al. Control-Logic Synthesis of Fully Programmable Valve Array Using Reinforcement Learning [J]. | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS , 2024 , 43 (1) : 277-290 . |
| MLA | Huang, Xing et al. "Control-Logic Synthesis of Fully Programmable Valve Array Using Reinforcement Learning" . | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 43 . 1 (2024) : 277-290 . |
| APA | Huang, Xing , Cai, Huayang , Guo, Wenzhong , Liu, Genggeng , Ho, Tsung-Yi , Chakrabarty, Krishnendu et al. Control-Logic Synthesis of Fully Programmable Valve Array Using Reinforcement Learning . | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS , 2024 , 43 (1) , 277-290 . |
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Image hiding aims to conceal one or more secret images within a cover image of the same resolution. Due to strict capacity requirements, image hiding is commonly called large-capacity steganography. In this paper, we propose StegFormer, a novel autoencoder-based image-hiding model. StegFormer can conceal one or multiple secret images within a cover image of the same resolution while preserving the high visual quality of the stego image. In addition, to mitigate the limitations of current steganographic models in real-world scenarios, we propose a normalizing training strategy and a restrict loss to improve the reliability of the steganographic models under realistic conditions. Furthermore, we propose an efficient steganographic capacity expansion method to increase the capacity of steganography and enhance the efficiency of secret communication. Through this approach, we can increase the relative payload of StegFormer to 96 bits per pixel without any training strategy modifications. Experiments demonstrate that our StegFormer outperforms existing state-of-the-art (SOTA) models. In the case of single-image steganography, there is an improvement of more than 3 dB and 5 dB in PSNR for secret/recovery image pairs and cover/stego image pairs. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Artificial intelligence Artificial intelligence Image enhancement Image enhancement Learning systems Learning systems Steganography Steganography
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| GB/T 7714 | Ke, Xiao , Wu, Huanqi , Guo, Wenzhong . StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography [C] . 2024 : 2723-2731 . |
| MLA | Ke, Xiao et al. "StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography" . (2024) : 2723-2731 . |
| APA | Ke, Xiao , Wu, Huanqi , Guo, Wenzhong . StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography . (2024) : 2723-2731 . |
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时空动作检测依赖于视频空间信息与时间信息的学习. 目前，最先进的基于卷积神经网络的动作检测器采用2D CNN或3D CNN架构，取得了显著的效果. 然而，由于网络结构的复杂性与时空信息感知的原因，这些方法通常采用非实时、离线的方式. 时空动作检测主要的挑战在于设计高效的检测网络架构，并能有效地感知融合时空特征. 考虑到上述问题，本文提出了一种基于时空交叉感知的实时动作检测方法. 该方法首先通过对输入视频进行乱序重排来增强时序信息，针对仅使用2D或3D骨干网络无法有效对时空特征进行建模，提出了基于时空交叉感知的多分支特征提取网络. 针对单一尺度时空特征描述性不足，提出一个多尺度注意力网络来学习长期的时间依赖和空间上下文信息. 针对时序和空间两种不同来源特征的融合，提出了一种新的运动显著性增强融合策略，对时空信息进行编码交叉映射，引导时序特征和空间特征之间的融合，突出更具辨别力的时空特征表示. 最后，基于帧级检测器结果在线计算动作关联性链接 . 本文提出的方法在两个时空动作数据集 UCF101-24 和 JHMDB-21 上分别达到了 84.71% 和78.4%的准确率，优于现有最先进的方法，并达到 73帧/秒的速度 . 此外，针对 JHMDB-21数据集存在高类间相似性与难样本数据易于混淆等问题，本文提出了基于动作表示的关键帧光流动作检测方法，避免了冗余光流的产生，进一步提升了动作检测准确率.
Keyword ：
多尺度注意力 多尺度注意力 实时动作检测 实时动作检测 时空交叉感知 时空交叉感知
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| GB/T 7714 | 柯逍 , 缪欣 , 郭文忠 . 基于时空交叉感知的实时动作检测方法 [J]. | 电子学报 , 2024 . |
| MLA | 柯逍 et al. "基于时空交叉感知的实时动作检测方法" . | 电子学报 (2024) . |
| APA | 柯逍 , 缪欣 , 郭文忠 . 基于时空交叉感知的实时动作检测方法 . | 电子学报 , 2024 . |
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The rising popularity of light field imaging underscores the pivotal role of image quality in user experience. However, evaluating the quality of light field images presents significant challenges owing to their highdimensional nature. Current quality assessment methods for light field images predominantly rely on machine learning or statistical analysis, often overlooking the interdependence among pixels. To overcome this limitation, we propose an innovative approach that employs a universal backbone network and introduces a dual-task framework for feature extraction. Specifically, we integrate a staged "primary-secondary" hierarchical evaluation mode into the universal backbone networks, enabling accurate quality score inference while preserving the intrinsic information of the original image. Our proposed approach reduces inference time by over 75% compared to existing methods, simultaneously achieving state-of-the-art results in terms of evaluation metrics. By harnessing the efficiency of neural networks, our framework offers an effective solution for the quality assessment of light field images, providing superior accuracy and speed compared to current methodologies.
Keyword ：
Deep learning Deep learning Image quality assessment Image quality assessment Light field images Light field images Multitasking mode Multitasking mode
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| GB/T 7714 | Guo, Wenzhong , Wang, Hanling , Ke, Xiao . Splitting the backbone: A novel hierarchical method for assessing light field image quality [J]. | OPTICS AND LASERS IN ENGINEERING , 2024 , 178 . |
| MLA | Guo, Wenzhong et al. "Splitting the backbone: A novel hierarchical method for assessing light field image quality" . | OPTICS AND LASERS IN ENGINEERING 178 (2024) . |
| APA | Guo, Wenzhong , Wang, Hanling , Ke, Xiao . Splitting the backbone: A novel hierarchical method for assessing light field image quality . | OPTICS AND LASERS IN ENGINEERING , 2024 , 178 . |
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Action quality assessment (AQA) is a challenging vision task due to the complexity and variance of the scoring rules embedded in the videos. Recent approaches have reduced the prediction difficulty of AQA via learning action differences between videos, but there are still challenges in learning scoring rules and capturing feature differences. To address these challenges, we propose a two -path target -aware contrastive regression (T2CR) framework. We propose to fuse direct and contrastive regression and exploit the consistency of information across multiple visual fields. Specifically, we first directly learn the relational mapping between global video features and scoring rules, which builds occupational domain prior knowledge to better capture local differences between videos. Then, we acquire the auxiliary visual fields of the videos through sparse sampling to learn the commonality of feature representations in multiple visual fields and eliminate the effect of subjective noise from a single visual field. To demonstrate the effectiveness of T2CR, we conduct extensive experiments on four AQA datasets (MTL-AQA, FineDiving, AQA-7, JIGSAWS). Our method is superior to state-of-the-art methods without elaborate structural design and fine-grained information.
Keyword ：
Action quality assessment Action quality assessment Multi-view information Multi-view information Video understanding Video understanding
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| GB/T 7714 | Ke, Xiao , Xu, Huangbiao , Lin, Xiaofeng et al. Two-path target-aware contrastive regression for action quality assessment [J]. | INFORMATION SCIENCES , 2024 , 664 . |
| MLA | Ke, Xiao et al. "Two-path target-aware contrastive regression for action quality assessment" . | INFORMATION SCIENCES 664 (2024) . |
| APA | Ke, Xiao , Xu, Huangbiao , Lin, Xiaofeng , Guo, Wenzhong . Two-path target-aware contrastive regression for action quality assessment . | INFORMATION SCIENCES , 2024 , 664 . |
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SMT is an optimized model for solving the routing problem of a multipin net in very large-scale integrated circuits. As the appearance of various obstacles on chips, the obstacle-avoiding problem has attracted much attention in recent years. Meanwhile, since interconnect delay plays a major role in chip delay, timing analysis is another critical problem worthy of consideration when constructing an Steiner minimum tree (SMT). Furthermore, the introduction of the X-architecture allows for better utilization of routing resources. In this article, a timing-driven obstacle-avoiding X-architecture Steiner minimum tree algorithm with slack constraints (TD-OAXSMT-SC) is proposed to consider obstacle-avoiding, timing slack constraints, and X-architecture simultaneously for the first time. The TD-OAXSMT-SC algorithm consists of four major stages: 1) in the routing tree initialization stage, this article constructs an X-architecture Prim-Dijkstra spanning tree as the initial routing tree with minimum total delay; 2) in the particle swarm optimization (PSO)-based routing tree iteration stage, a novel discrete PSO algorithm based on genetic operators is proposed to obtain a high-quality routing tree; 3) in the routing tree standardization stage, two effective standardization strategies are proposed to obtain a routing tree that satisfies both obstacle-avoiding and timing slack constraints; and 4) in the routing tree optimization stage, the connection of interconnected wires is optimized in a global manner, thus obtaining an optimized routing tree. Experimental results show that the proposed TD-OAXSMT-SC algorithm outperforms the state-of-the-art methods in routing quality with slack constraints.
Keyword ：
Delays Delays Integrated circuit interconnections Integrated circuit interconnections Obstacle-avoiding Obstacle-avoiding Optimization Optimization Pins Pins PSO PSO Routing Routing SMT SMT timing-driven routing timing-driven routing timing slack constraints timing slack constraints Very large scale integration Very large scale integration Wires Wires X-architecture routing X-architecture routing
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| GB/T 7714 | Zhu, Yuhan , Liu, Genggeng , Lu, Ren et al. Timing-Driven Obstacle-Avoiding X-Architecture Steiner Minimum Tree Algorithm With Slack Constraints [J]. | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
| MLA | Zhu, Yuhan et al. "Timing-Driven Obstacle-Avoiding X-Architecture Steiner Minimum Tree Algorithm With Slack Constraints" . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2024) . |
| APA | Zhu, Yuhan , Liu, Genggeng , Lu, Ren , Huang, Xing , Gan, Min , Guo, Wenzhong . Timing-Driven Obstacle-Avoiding X-Architecture Steiner Minimum Tree Algorithm With Slack Constraints . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
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Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in the real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this article, we apply Graph Convolutional Network (GCN) to cope with heterogeneous graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top-k nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module, and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks, and the experimental results indicate that MGCN-DNS achieves pleasurable performance on several benchmark multi-view datasets.
Keyword ：
differentiable node selection differentiable node selection graph convolutional network graph convolutional network Multi-view learning Multi-view learning semi-supervised classification semi-supervised classification
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| GB/T 7714 | Chen, Zhaoliang , Fu, Lele , Xiao, Shunxin et al. Multi-View Graph Convolutional Networks with Differentiable Node Selection [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2024 , 18 (1) . |
| MLA | Chen, Zhaoliang et al. "Multi-View Graph Convolutional Networks with Differentiable Node Selection" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 18 . 1 (2024) . |
| APA | Chen, Zhaoliang , Fu, Lele , Xiao, Shunxin , Wang, Shiping , Plant, Claudia , Guo, Wenzhong . Multi-View Graph Convolutional Networks with Differentiable Node Selection . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2024 , 18 (1) . |
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Semi-supervised classification aims to leverage a small amount of labeled data for learning tasks. Multi-view semi-supervised classification has attracted widespread attention because it can exploit multi-view data to optimize the classification performance. However, its methods are often ineffective when facing extremely limited labeled samples. In this paper, we propose a novel multi-view semi-supervised classification model via auto-weighted submarkov random walk. The proposed method can utilize similar nodes, spread information among nodes on graphs and exploit multi-view data with less labeled information. Accordingly, it enables an effective exploitation of both a small number of labeled data and a large amount of unlabeled data by connecting them to designed auxiliary nodes. Furthermore, an ideal weight on the Hellinger distance is allocated to each view data for obtaining a global label indicator matrix, which is expected to be robust to imbalanced classes. Compared with existing state-of-the-art methods, extensive experiments on six widely used datasets are conducted to verify the superiority of the proposed method. © 2024 Elsevier Ltd
Keyword ：
Machine learning Machine learning Markov process Markov process Multi-view learning Multi-view learning Random walk Random walk Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Chen, W. , Cai, Z. , Lin, P. et al. Multi-view semi-supervised classification via auto-weighted submarkov random walk [J]. | Expert Systems with Applications , 2024 , 256 . |
| MLA | Chen, W. et al. "Multi-view semi-supervised classification via auto-weighted submarkov random walk" . | Expert Systems with Applications 256 (2024) . |
| APA | Chen, W. , Cai, Z. , Lin, P. , Huang, Y. , Du, S. , Guo, W. et al. Multi-view semi-supervised classification via auto-weighted submarkov random walk . | Expert Systems with Applications , 2024 , 256 . |
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三维点云数据在自动驾驶、机器人和高精地图等领域得到了广泛应用。目前，基于深度学习的三维点云数据处理主要基于有监督学习，其算法性能依赖于大规模高质量的标注数据集。此外，仅在单一设备与场景中训练的三维点云数据处理模型难以应用于不同设备与环境，泛化性能有限。因此，如何减少三维点云标注数据集的需求以及提高三维点云处理模型的适应性是当前三维点云数据处理面临的重要难题。作为迁移学习的一个重要分支，域自适应学习旨在不同域间特征分布存在差异的情况下提高模型的适应性，可为解决上述难题提供重要思路。为便于对点云域自适应学习领域进行更深入有效的探索，本文主要从对抗学习、跨模态学习、伪标签学习、数据对齐及其他方法 5个方面对近年来的三维点云域自适应学习方法进行了系统梳理与分类归纳，并分析总结每类点云域自适应学习方法所具备的优势及面临的问题。最后，对三维点云域自适应学习研究领域的未来发展进行了展望。
Keyword ：
三维点云 三维点云 伪标签学习 伪标签学习 域自适应学习 域自适应学习 对抗学习 对抗学习 数据对齐 数据对齐 跨模态学习 跨模态学习 遥感 遥感
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| GB/T 7714 | 范文辉 , 林茜 , 罗欢 et al. 面向三维点云的域自适应学习 [J]. | 遥感学报 , 2024 , 28 (04) : 825-842 . |
| MLA | 范文辉 et al. "面向三维点云的域自适应学习" . | 遥感学报 28 . 04 (2024) : 825-842 . |
| APA | 范文辉 , 林茜 , 罗欢 , 郭文忠 , 汪汉云 , 戴晨光 . 面向三维点云的域自适应学习 . | 遥感学报 , 2024 , 28 (04) , 825-842 . |
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