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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Guangwu Wang Yu Gu Wei Wang Zhi Yu Shuyi Song China Braille Library Beijing China School of Software Technology Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China DBAPP Security Ltd College of Computer Science Zhejiang University Hangzhou China

This paper presents BrailleNet, an enhanced one-stage anchor-based object detection model for Braille character recognition, incorporating foreground attention and semantic learning. BrailleNet is validated using two new datasets: mobile phone live shot Braille and chart Braille. Experimental results show BrailleNet's superior performance on both public and introduced datasets.

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CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks

arXiv

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arXiv 2023年

作者： Bei, Yuanchen Xu, Hao Zhou, Sheng Chi, Huixuan Wang, Haishuai Zhang, Mengdi Li, Zhao Bu, Jiajun Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science and Technology Zhejiang University China Meituan China School of Software Technology Zhejiang University China Institute of Computing Technology Chinese Academy of Science China

Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the advances in dynamic graph neural networks (DGNNs), the rich information and diverse downstream tasks have posed significant difficulties for the practical application of DGNNs in industrial scenarios. To this end, in this paper, we propose to address them by pre-training and present the Contrastive Pre-Training Method for Dynamic Graph Neural Networks (CPDG). CPDG tackles the challenges of pre-training for DGNNs, including generalization capability and long-short term modeling capability, through a flexible structural-temporal subgraph sampler along with structural-temporal contrastive pre-training schemes. Extensive experiments conducted on both large-scale research and industrial dynamic graph datasets show that CPDG outperforms existing methods in dynamic graph pre-training for various downstream tasks under three transfer settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis

arXiv

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arXiv 2023年

作者： Liu, Ke Liu, Feng Wang, Haishuai Ma, Ning Bu, Jiajun Han, Bo Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China School of Computing and Information Systems The University of Melbourne Australia Department of Computer Science Hong Kong Baptist University Hong Kong

Implicit neural representations (INRs) aim to learn a continuous function (i.e., a neural network) to represent an image, where the input and output of the function are pixel coordinates and RGB/Gray values, respectively. However, images tend to consist of many objects whose colors are not perfectly consistent, resulting in the challenge that image is actually a discontinuous piecewise function and cannot be well estimated by a continuous function. In this paper, we empirically investigate that if a neural network is enforced to fit a discontinuous piecewise function to reach a fixed small error, the time costs will increase exponentially with respect to the boundaries in the spatial domain of the target signal. We name this phenomenon the exponential-increase hypothesis. Under the exponential-increase hypothesis, learning INRs for images with many objects will converge very slowly. To address this issue, we first prove that partitioning a complex signal into several sub-regions and utilizing piecewise INRs to fit that signal can significantly speed up the convergence. Based on this fact, we introduce a simple partition mechanism to boost the performance of two INR methods for image reconstruction: one for learning INRs, and the other for learning-to-learn INRs. In both cases, we partition an image into different sub-regions and dedicate smaller networks for each part. In addition, we further propose two partition rules based on regular grids and semantic segmentation maps, respectively. Extensive experiments validate the effectiveness of the proposed partitioning methods in terms of learning INR for a single image (ordinary learning framework) and the learning-to-learn framework. Code is released here. © 2023, CC BY-NC-SA.

关键词： Image reconstruction

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Image Search with Text Feedback by Deep Hierarchical Attention Mutual Information Maximization 21

Image Search with Text Feedback by Deep Hierarchical Attenti...

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29th ACM International Conference on Multimedia, MM 2021

作者： Gu, Chunbin Bu, Jiajun Zhang, Zhen Yu, Zhi Ma, Dongfang Wang, Wei Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China Alibaba- Zhejiang University Joint Institute of Frontier Technologies China Zhejiang Provincial Key Laboratory of Service Robot School of Software Technology Ningbo Research Institute Zhejiang University China Institute of Marine Sensing and Networking Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Zhejiang University China

ISBN: (纸本)9781450386517

Image retrieval with text feedback is an emerging research topic with the objective of integrating inputs from multiple modalities as queries. In this paper, queries contain a reference image plus text feedback that describes modifications between this image and the desired image. The existing work for this task mainly focuses on designing a new fusion network to compose the image and text. Still, little research pays attention to the modality gap caused by the inconsistent distribution of features from different modalities, which dramatically influences the feature fusion and similarity learning between queries and the desired image. We propose a Distribution-Aligned Text-based Image Retrieval (DATIR) model, which consists of attention mutual information maximization and hierarchical mutual information maximization, to bridge this gap by increasing non-linear statistic dependencies between representations of different modalities. More specifically, attention mutual information maximization narrows the modality gap between different input modalities by maximizing mutual information between the text representation and its semantically consistent representation captured from the reference image and the desired image by the difference transformer. For hierarchical mutual information maximization, it aligns distributions of features from the image modality and the fusion modality by estimating mutual information between a single-layer representation in the fusion network and the multi-level representations in the desired image encoder. Extensive experiments on three large-scale benchmark datasets demonstrate that we can bridge the modality gap between different modalities and achieve state-of-the-art retrieval performance. © 2021 ACM.

关键词： Image fusion

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Research on Hybrid Flow-shop Scheduling Based on INSGA-II Algorithm 4

Research on Hybrid Flow-shop Scheduling Based on INSGA-II Al...

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4th International Conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Chen, Haopeng Fang, Zaojun Zhong, Caiming Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo Zhejiang China Zhejiang Key Laboratory of Robot and Intelligent Manufacturing Equipment Technology Ningbo Zhejiang China College of Science and Technology Ningbo University Ningbo Zhejiang China

ISBN: (纸本)9781450398343

Aiming at the hybrid flow shop scheduling problem (HFSP), a multi-objective optimization scheduling model is established with the objectives of minimizing makespan, energy consumption and total load, and an improved non-dominated sorting genetic algorithm-II (INSGA-II) is proposed to solve the problem. Combining the problem characteristics of task sequencing and equipment selection in the production process of hybrid flow shop, a double-layer coding rule combining the job sequence code (JSC) and machine allocation code (MAC) is adopted. Different crossover and mutation operators are used for JSC and MAC respectively to improve the global search capability of the algorithm. Aiming at the poor local search capability of NSGA-II, neighborhood search is introduced to improve the quality of population. Finally, the optimal parameters combination of the algorithm is determined by orthogonal experiment, and the feasibility and effectiveness of INSGA-II for solving the multi-objective scheduling problem in hybrid flow shop are verified by simulation experiments. © 2022 ACM.

关键词： Multiobjective optimization

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Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis

Partition Speeds Up Learning Implicit Neural Representations...

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International Conference on computer Vision (ICCV)

作者： Ke Liu Feng Liu Haishuai Wang Ning Ma Jiajun Bu Bo Han Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China School of Computing and Information Systems The University of Melbourne Australia Department of Computer Science Hong Kong Baptist University Hong Kong SAR China

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An Intelligent Blind Glasses System Based on Stair & Step Recognition Function

An Intelligent Blind Glasses System Based on Stair & Step Re...

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Signal Processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Yueqing Zhang Zhi Yu Wei Wang Jiajun Bu Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China School of Software Technology Zhejiang University Hangzhou Zhejiang China

In this paper, an intelligent blind glasses system based on stair & step recognition is proposed, which combines the innovative appearance of the product and the high performance advantages of functional modules to effectively assist visually impaired users to safely avoid obstacles when they encounter stairs or steps in indoor environments.

关键词： Headphones Engineering management Lighting Glass Stairs Signal processing Bones

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A Traffic Light Recognition Method Based on Image Super Resolution in Intelligent Blind Glasses System

A Traffic Light Recognition Method Based on Image Super Reso...

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Advances in Informatics, Electronics and Education (ISAIEE), International Symposium on

One kind of traffic light recognition method based on super resolution technology applied in intelligent eyeglasses system is proposed in this paper. Take advantage of the high recognition accuracy of the latest hot target detectors and the ability of super-resolution technology which can increase image resolution dramatically, so as to achieve the accurate identification of pedestrian traffic lights. The lightweight adjustment operation can ensure the real-time performance of the model on the mobile terminal and correctly assist the safe passage of visually impaired people at traffic intersections.

关键词： Image recognition Target recognition Image color analysis Superresolution Education Glass Detectors

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Hilbert Distillation for Cross-Dimensionality Networks

arXiv

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arXiv 2022年

作者： Qin, Dian Wang, Haishuai Liu, Zhe Xu, Hongjia Zhou, Sheng Bu, Jiajun Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China

3D convolutional neural networks have revealed superior performance in processing volumetric data such as video and medical imaging. However, the competitive performance by leveraging 3D networks results in huge computational costs, which are far beyond that of 2D networks. In this paper, we propose a novel Hilbert curve-based cross-dimensionality distillation approach that facilitates the knowledge of 3D networks to improve the performance of 2D networks. The proposed Hilbert Distillation (HD) method preserves the structural information via the Hilbert curve, which maps high-dimensional (>=2) representations to one-dimensional continuous space-filling curves. Since the distilled 2D networks are supervised by the curves converted from dimensionally heterogeneous 3D features, the 2D networks are given an informative view in terms of learning structural information embedded in well-trained high-dimensional representations. We further propose a Variable-length Hilbert Distillation (VHD) method to dynamically shorten the walking stride of the Hilbert curve in activation feature areas and lengthen the stride in context feature areas, forcing the 2D networks to pay more attention to learning from activation features. The proposed algorithm outperforms the current state-of-the-art distillation techniques adapted to cross-dimensionality distillation on two classification tasks. Moreover, the distilled 2D networks by the proposed method achieve competitive performance with the original 3D networks, indicating the lightweight distilled 2D networks could potentially be the substitution of cumbersome 3D networks in the real-world scenario. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Heterophilous Distribution Propagation for Graph Neural Networks

SSRN

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SSRN 2024年

作者： Zheng, Zhuonan Zhou, Sheng Xu, Hongjia Gu, Ming Xu, Yilun Li, Ao Li, Yuhong Gu, Jingjun Bu, Jiajun College of Computer Science Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University Hangzhou310027 China School of Software Technology Zhejiang University Ningbo315048 China Bytedance Inc. Hangzhou311113 China Alibaba Group Hangzhou310052 China

Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar behaviors, while it may be violated in many real-world graphs. Recently, heterophilous graph neural networks (HeterGNNs) have attracted increasing attention by modifying the neural message passing schema for heterophilous neighborhoods. However, they suffer from insufficient neighborhood partition and heterophily modeling, both of which are critical but challenging to break through. To tackle these challenges, in this paper, we propose heterophilous distribution propagation (HDP) for graph neural networks. Instead of aggregating information from all neighborhoods, HDP adaptively separates the neighbors into homophilous and heterphilous parts based on the pseudo assignments during training. The heterophilous neighborhood distribution is learned with orthogonality-oriented constraint via a trusted prototype contrastive learning paradigm. Both the homophilous and heterophilous patterns are propagated with a novel semantic-aware message passing mechanism. We conduct extensive experiments on 9 benchmark datasets with different levels of homophily. Experimental results show that our method outperforms representative baselines on heterophilous datasets. © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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