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Article ; Online: Frequency Domain-Oriented Complex Graph Neural Networks for Graph Classification.

Liu, Youfa / Du, Bo

IEEE transactions on neural networks and learning systems

2024 Volume PP

Abstract: Graph neural networks (GNNs) could directly deal with the data of graph structure. Current GNNs are confined to the spatial domain and learn real low-dimensional embeddings in graph classification tasks. In this article, we explore frequency domain- ... ...

Abstract	Graph neural networks (GNNs) could directly deal with the data of graph structure. Current GNNs are confined to the spatial domain and learn real low-dimensional embeddings in graph classification tasks. In this article, we explore frequency domain-oriented complex GNNs in which the node's embedding in each layer is a complex vector. The difficulty lies in the design of graph pooling and we propose a mirror-connected design with two crucial problems: parameter reduction problem and complex gradient backpropagation problem. To deal with the former problem, we propose the notion of squared singular value pooling (SSVP) and prove that the representation power of SSVP followed by a fully connected layer with nonnegative weights is exactly equivalent to that of a mirror-connected layer. To resolve the latter problem, we provide an alternative feasible method to solve singular values of complex embeddings with a theoretical guarantee. Finally, we propose a mixture of pooling strategies in which first-order statistics information is employed to enrich the last low-dimensional representation. Experiments on benchmarks demonstrate the effectiveness of the complex GNNs with mirror-connected layers.
Language	English
Publishing date	2024-01-19
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2024.3351762
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Article ; Online: [Retracted] MicroRNA‑509 acts as a tumor suppressor in tongue squamous cell carcinoma by targeting epidermal growth factor receptor.

Hou, Chao / Dong, Yan / Zhang, Fenghe / Du, Bo

Molecular medicine reports

2024 Volume 29, Issue 3

Abstract: Following the publication of this paper, it was drawn to the Editor's attention by a concerned reader that certain of the Transwell cell invasion assay data shown in Fig. 2C on p. 7248 and Fig. 3G on p. 7249 were strikingly similar to data in different ... ...

Abstract	Following the publication of this paper, it was drawn to the Editor's attention by a concerned reader that certain of the Transwell cell invasion assay data shown in Fig. 2C on p. 7248 and Fig. 3G on p. 7249 were strikingly similar to data in different form in other articles written by different authors at different research institutes, which had either already been published (some of which have now been retracted), or had been submitted for publication at around the same time. Owing to the fact that certain of the data in the above article had already been published prior to its submission to
Language	English
Publishing date	2024-02-01
Publishing country	Greece
Document type	Retraction of Publication
ZDB-ID	2469505-1
ISSN	1791-3004 ; 1791-2997
ISSN (online)	1791-3004
ISSN	1791-2997
DOI	10.3892/mmr.2024.13176
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Article ; Online: Robust Self-Ensembling Network for Hyperspectral Image Classification.

Xu, Yonghao / Du, Bo / Zhang, Liangpei

IEEE transactions on neural networks and learning systems

2024 Volume 35, Issue 3, Page(s) 3780–3793

Abstract: Recent research has shown the great potential of deep learning algorithms in the hyperspectral image (HSI) classification task. Nevertheless, training these models usually requires a large amount of labeled data. Since the collection of pixel-level ... ...

Abstract	Recent research has shown the great potential of deep learning algorithms in the hyperspectral image (HSI) classification task. Nevertheless, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSI is laborious and time-consuming, developing algorithms that can yield good performance in the small sample size situation is of great significance. In this study, we propose a robust self-ensembling network (RSEN) to address this problem. The proposed RSEN consists of two subnetworks including a base network and an ensemble network. With the constraint of both the supervised loss from the labeled data and the unsupervised loss from the unlabeled data, the base network and the ensemble network can learn from each other, achieving the self-ensembling mechanism. To the best of our knowledge, the proposed method is the first attempt to introduce the self-ensembling technique into the HSI classification task, which provides a different view on how to utilize the unlabeled data in HSI to assist the network training. We further propose a novel consistency filter to increase the robustness of self-ensembling learning. Extensive experiments on three benchmark HSI datasets demonstrate that the proposed algorithm can yield competitive performance compared with the state-of-the-art methods.
Language	English
Publishing date	2024-02-29
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2022.3198142
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SketchTrans: Disentangled Prototype Learning With Transformer for Sketch-Photo Recognition.

Chen, Cuiqun / Ye, Mang / Qi, Meibin / Du, Bo

IEEE transactions on pattern analysis and machine intelligence

2024 Volume 46, Issue 5, Page(s) 2950–2964

Abstract: Matching hand-drawn sketches with photos (a.k.a sketch-photo recognition or re-identification) faces the information asymmetry challenge due to the abstract nature of the sketch modality. Existing works tend to learn shared embedding spaces with CNN ... ...

Abstract	Matching hand-drawn sketches with photos (a.k.a sketch-photo recognition or re-identification) faces the information asymmetry challenge due to the abstract nature of the sketch modality. Existing works tend to learn shared embedding spaces with CNN models by discarding the appearance cues for photo images or introducing GAN for sketch-photo synthesis. The former unavoidably loses discriminability, while the latter contains ineffaceable generation noise. In this paper, we start the first attempt to design an information-aligned sketch transformer (SketchTrans
Language	English
Publishing date	2024-04-03
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2023.3337005
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Channel Augmentation for Visible-Infrared Re-Identification.

Ye, Mang / Wu, Zesen / Chen, Cuiqun / Du, Bo

IEEE transactions on pattern analysis and machine intelligence

2024 Volume 46, Issue 4, Page(s) 2299–2315

Abstract: This paper introduces a simple yet powerful channel augmentation for visible-infrared re-identification. Most existing augmentation operations designed for single-modality visible images do not fully consider the imagery properties in visible to infrared ...

Abstract	This paper introduces a simple yet powerful channel augmentation for visible-infrared re-identification. Most existing augmentation operations designed for single-modality visible images do not fully consider the imagery properties in visible to infrared matching. Our basic idea is to homogeneously generate color-irrelevant images by randomly exchanging the color channels. It can be seamlessly integrated into existing augmentation operations, consistently improving the robustness against color variations. For cross-modality metric learning, we design an enhanced channel-mixed learning strategy to simultaneously handle the intra- and cross-modality variations with squared difference for stronger discriminability. Besides, a weak-and-strong augmentation joint learning strategy is further developed to explicitly optimize the outputs of augmented images, which mutually integrates the channel augmented images (strong) and the general augmentation operations (weak) with consistency regularization. Furthermore, by conducting the label association between the channel augmented images and infrared modalities with modality-specific clustering, a simple yet effective unsupervised learning baseline is designed, which significantly outperforms existing unsupervised single-modality solutions. Extensive experiments with insightful analysis on two visible-infrared recognition tasks show that the proposed strategies consistently improve the accuracy. Without auxiliary information, the Rank-1/mAP achieves 71.48%/68.15% on the large-scale SYSU-MM01 dataset.
Language	English
Publishing date	2024-03-06
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2023.3332875
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Adversarial pair-wise distribution matching for remote sensing image cross-scene classification.

Zhu, Sihan / Wu, Chen / Du, Bo / Zhang, Liangpei

Neural networks : the official journal of the International Neural Network Society

2024 Volume 174, Page(s) 106241

Abstract: Remarkable achievements have been made in the field of remote sensing cross-scene classification in recent years. However, most methods directly align the entire image features for cross-scene knowledge transfer. They usually ignore the high background ... ...

Abstract	Remarkable achievements have been made in the field of remote sensing cross-scene classification in recent years. However, most methods directly align the entire image features for cross-scene knowledge transfer. They usually ignore the high background complexity and low category consistency of remote sensing images, which can significantly impair the performance of distribution alignment. Besides, shortcomings of the adversarial training paradigm and the inability to guarantee the prediction discriminability and diversity can also hinder cross-scene classification performance. To alleviate the above problems, we propose a novel cross-scene classification framework in a discriminator-free adversarial paradigm, called Adversarial Pair-wise Distribution Matching (APDM), to avoid irrelevant knowledge transfer and enable effective cross-domain modeling. Specifically, we propose the pair-wise cosine discrepancy for both inter-domain and intra-domain prediction measurements to fully leverage the prediction information, which can suppress negative semantic features and implicitly align the cross-scene distributions. Nuclear-norm maximization and minimization are introduced to enhance the target prediction quality and increase the applicability of the source knowledge, respectively. As a general cross-scene framework, APDM can be easily embedded with existing methods to boost the performance. Experimental results and analyses demonstrate that APDM can achieve competitive and effective performance on cross-scene classification tasks.
MeSH term(s)	Remote Sensing Technology ; Knowledge ; Semantics
Language	English
Publishing date	2024-03-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2024.106241
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Multi-tailed vision transformer for efficient inference.

Wang, Yunke / Du, Bo / Wang, Wenyuan / Xu, Chang

Neural networks : the official journal of the International Neural Network Society

2024 Volume 174, Page(s) 106235

Abstract: Recently, Vision Transformer (ViT) has achieved promising performance in image recognition and gradually serves as a powerful backbone in various vision tasks. To satisfy the sequential input of Transformer, the tail of ViT first splits each image into a ...

Abstract	Recently, Vision Transformer (ViT) has achieved promising performance in image recognition and gradually serves as a powerful backbone in various vision tasks. To satisfy the sequential input of Transformer, the tail of ViT first splits each image into a sequence of visual tokens with a fixed length. Then, the following self-attention layers construct the global relationship between tokens to produce useful representation for the downstream tasks. Empirically, representing the image with more tokens leads to better performance, yet the quadratic computational complexity of self-attention layer to the number of tokens could seriously influence the efficiency of ViT's inference. For computational reduction, a few pruning methods progressively prune uninformative tokens in the Transformer encoder, while leaving the number of tokens before the Transformer untouched. In fact, fewer tokens as the input for the Transformer encoder can directly reduce the following computational cost. In this spirit, we propose a Multi-Tailed Vision Transformer (MT-ViT) in the paper. MT-ViT adopts multiple tails to produce visual sequences of different lengths for the following Transformer encoder. A tail predictor is introduced to decide which tail is the most efficient for the image to produce accurate prediction. Both modules are optimized in an end-to-end fashion, with the Gumbel-Softmax trick. Experiments on ImageNet-1K demonstrate that MT-ViT can achieve a significant reduction on FLOPs with no degradation of the accuracy and outperform compared methods in both accuracy and FLOPs.
MeSH term(s)	Recognition, Psychology
Language	English
Publishing date	2024-03-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2024.106235
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 2365: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Positive Periodic Solution for Second-Order Nonlinear Differential Equations with Variable Coefficients and Mixed Delays.

Dai, Zejian / Du, Bo

Entropy (Basel, Switzerland)

2022 Volume 24, Issue 9

Abstract: In this paper, we study two types of second-order nonlinear differential equations with variable coefficients and mixed delays. Based on Krasnoselskii's fixed point theorem, the existence results of positive periodic solution are established. It should ... ...

Abstract	In this paper, we study two types of second-order nonlinear differential equations with variable coefficients and mixed delays. Based on Krasnoselskii's fixed point theorem, the existence results of positive periodic solution are established. It should be pointed out that the equations we studied are more general. Therefore, the results of this paper have better applicability.
Language	English
Publishing date	2022-09-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e24091286
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SAAN: Similarity-Aware Attention Flow Network for Change Detection With VHR Remote Sensing Images.

Guo, Haonan / Su, Xin / Wu, Chen / Du, Bo / Zhang, Liangpei

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

2024 Volume 33, Page(s) 2599–2613

Abstract: Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically extract bi-temporal image features using a weight-sharing Siamese encoder ...

Abstract	Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically extract bi-temporal image features using a weight-sharing Siamese encoder network and identify change regions using a decoder network. These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions; and 2) the models' confidence in the change regions is inconsistent at different decoder stages. The first problem is because deep encoder layers cannot effectively learn from imbalanced change categories using the sole output supervision, while the second problem is attributed to the lack of explicit semantic consistency preservation. To address these issues, we design a novel similarity-aware attention flow network (SAAN). SAAN incorporates a similarity-guided attention flow module with deeply supervised similarity optimization to achieve effective change detection. Specifically, we counter the first issue by explicitly guiding deep encoder layers to discover semantic relations from bi-temporal input images using deeply supervised similarity optimization. The extracted features are optimized to be semantically similar in the unchanged regions and dissimilar in the changing regions. The second drawback can be alleviated by the proposed similarity-guided attention flow module, which incorporates similarity-guided attention modules and attention flow mechanisms to guide the model to focus on discriminative channels and regions. We evaluated the effectiveness and generalization ability of the proposed method by conducting experiments on a wide range of CD tasks. The experimental results demonstrate that our method achieves excellent performance on several CD tasks, with discriminative features and semantic consistency preserved.
Language	English
Publishing date	2024-04-01
Publishing country	United States
Document type	Journal Article
ISSN	1941-0042
ISSN (online)	1941-0042
DOI	10.1109/TIP.2024.3349868
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Towards a better negative sampling strategy for dynamic graphs.

Gao, Kuang / Liu, Chuang / Wu, Jia / Du, Bo / Hu, Wenbin

Neural networks : the official journal of the International Neural Network Society

2024 Volume 173, Page(s) 106175

Abstract: As dynamic graphs have become indispensable in numerous fields due to their capacity to represent evolving relationships over time, there has been a concomitant increase in the development of Temporal Graph Neural Networks (TGNNs). When training TGNNs ... ...

Abstract	As dynamic graphs have become indispensable in numerous fields due to their capacity to represent evolving relationships over time, there has been a concomitant increase in the development of Temporal Graph Neural Networks (TGNNs). When training TGNNs for dynamic graph link prediction, the commonly used negative sampling method often produces starkly contrasting samples, which can lead the model to overfit these pronounced differences and compromise its ability to generalize effectively to new data. To address this challenge, we introduce an innovative negative sampling approach named Enhanced Negative Sampling (ENS). This strategy takes into account two pervasive traits observed in dynamic graphs: (1) Historical dependence, indicating that nodes frequently reestablish connections they held in the past, and (2) Temporal proximity preference, which posits that nodes are more inclined to connect with those they have recently interacted with. Specifically, our technique employs a designed scheduling function to strategically control the progression of difficulty of the negative samples throughout the training. This ensures that the training progresses in a balanced manner, becoming incrementally challenging, and thereby enhancing TGNNs' proficiency in predicting links within dynamic graphs. In our empirical evaluation across multiple datasets, we discerned that our ENS, when integrated as a modular component, notably augments the performance of four SOTA baselines. Additionally, we further investigated the applicability of ENS in handling dynamic graphs of varied attributes. Our code is available at https://github.com/qqaazxddrr/ENS.
MeSH term(s)	Neural Networks, Computer
Language	English
Publishing date	2024-02-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2024.106175
Database	MEDical Literature Analysis and Retrieval System OnLINE

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ab Jg. 2022: Lesesaal (EG)

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