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International Conference on computer Supported Cooperative Work in Design

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image-text matching is an important task in cross-modal information processing, which consists of evaluating the similarity between images and text. However, the data of the two modalities have different distributions and representations, which cannot be directly compared. Therefore, the two modalities need to be processed separately. Most existing image-text matching methods extract image fragments and text fragments separately, using an attention mechanism to establish relationships between regions in the image and words in the text. Subsequently, they aggregate the similarity of these images and text fragments. Regardless, not all fragment alignment is meaningful, and irrelevant fragment alignment will bring redundancy and reduce retrieval accuracy. In addition to that, there are contextual relationships between image regions and between text words, which should also be taken into account. In this paper, we simultaneously consider these two aspects and propose a Semantic-Enhanced Attention Network (SEAN). It first focuses on each modality, mining contextual relationships between fragments within each modality and aggregating contextual information into visual and textual embeddings. Then, we compute the similarity between all image regions and text fragments, focusing all attention on the region-word pairs with the highest similarity. Finally, we summarize these to infer the similarity between the image and text. Our method achieves competitive results on two generalized image text retrieval datasets, Flickr30K and MS-COCO.

关键词： Visualization Attention mechanisms Federated learning Computational modeling Aggregates Semantics Redundancy

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Evolving multi-resolution pooling CNN for monaural singing voice separation

arXiv

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arXiv 2020年

作者： Yuan, Weitao Dong, Bofei Wang, Shengbei Unoki, Masashi Wang, Wenwu Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin Polytechnic University Tianjin China School of Information Science Japan Advanced Institute of Science and Technology Japan Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

Monaural Singing Voice Separation (MSVS) is a challenging task and has been studied for decades. Deep neural networks (DNNs) are the current state-of-the-art methods for MSVS. However, the existing DNNs are often designed manually, which is time-consuming and error-prone. In addition, the network architectures are usually pre-defined, and not adapted to the training data. To address these issues, we introduce a Neural Architecture Search (NAS) method to the structure design of DNNs for MSVS. Specifically, we propose a new multi-resolution Convolutional Neural Network (CNN) framework for MSVS namely Multi-Resolution Pooling CNN (MRP-CNN), which uses various-size pooling operators to extract multi-resolution features. Based on the NAS, we then develop an evolving framework namely Evolving MRP-CNN (E-MRP-CNN), by automatically searching the effective MRP-CNN structures using genetic algorithms, optimized in terms of a single-objective considering only separation performance, or multi-objective considering both the separation performance and the model complexity. The multiobjective E-MRP-CNN gives a set of Pareto-optimal solutions, each providing a trade-off between separation performance and model complexity. Quantitative and qualitative evaluations on the MIR-1K and DSD100 datasets are used to demonstrate the advantages of the proposed framework over several recent baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception

arXiv

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arXiv 2024年

作者： Li, Haijie Wu, Yanmin Meng, Jiarui Gao, Qiankun Zhang, Zhiyao Wang, Ronggang Zhang, Jian School of Electronic and Computer Engineering Peking University China Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology Peking University Shenzhen Graduate School China College of Information Science and Engineering Northeastern University China

3D scene understanding is vital for applications in autonomous driving, robotics, and augmented reality. However, scene understanding based on 3D Gaussian Splatting faces three key challenges: (i) an imbalance between appearance and semantics, (ii) inconsistencies in object boundaries, and (iii) difficulties with top-down instance segmentation. To address these challenges, we propose InstanceGaussian, a method that jointly learns appearance and semantic features while adaptively aggregating instances. Our contributions are as follows: (i) a new Semantic-Scaffold-GS representation to improve feature representation and boundary delineation, (ii) a progressive training strategy for enhanced stability and segmentation, and (iii) a category-agnostic, bottom-up instance aggregation approach for better segmentation. Experimental results demonstrate that our approach achieves state-of-the-art performance in category-agnostic, open-vocabulary 3D point-level segmentation, validating the effectiveness of our proposed method. Project page: https://***/InstanceGaussian/ Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Novel Microwave Sensor for Traditional Chinese Medicine Decoctions Detection

A Novel Microwave Sensor for Traditional Chinese Medicine De...

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IEEE International Workshop on Radio-Frequency Integration technology (RFIT)

作者： Ming-Feng Xu Jing Wu Hua-Feng Li Jin-Peng He Zhong-Qi Zhang Min Chen Rui-Sen Chen Guan-Long Huang Zi-Yu Pang Kam-Weng Tam School of Electronics and Information Engineering Foshan University Foshan Guangdong China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology Foshan University Foshan China Department of Electrical and Computer Engineering University of Macau Macau SAR China

ISBN: (数字)9798331541095

ISBN: (纸本)9798331541101

This paper demonstrates a high-sensitivity substrate-integrated waveguide (SIW) microwave sensor for traditional Chinese medicine decoctions detection. The designed microwave sensor is based on elliptical electric-LC(EELC) and interdigital capacitors (IDC). The regent under test (RUT) with different permittivities from 2 to 5 and dimensions of 9 mm $\times 11$ mm $\times 0.8$ mm is placed on the sensitive area of the sensor. According to the simulated result, the resonant frequency of the sensor shifts between 4.06 and 5.63 GHz with the variation of the RUT permittivity. The designed sensor can achieve sensitivity up to 8.28%, which shows a good feasibility in detecting liquid dielectric properties.

关键词： Radio frequency Sensitivity Dielectric constant Capacitors Resonant frequency Metals Microwave sensors Sensors Permittivity Substrates

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Document-Level Event Extraction Based on Frame Semantic Mapping and Type Awareness 21

Document-Level Event Extraction Based on Frame Semantic Mapp...

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21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Lu, Jiang Li, Ru Su, Xuefeng Yan, Zhichao Chen, Jiaxing School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China School of Modern Logistics Shanxi Vocational University of Engineering Science and Technology Shanxi Jinzhong030609 China

Document-Level event extraction is the identification of its event type and event arguments from a given text. At present, the problems of sparse data and multi-event argument coupling are most in Document-Level events. Based on this, We map Chinese FrameNet with Chinese text events, and the sliding window mechanism and trigger word Paraphrase are introduced to improve the data sparseness problem of event detection. The use of a multi-event separation strategy based on type-aware labels alleviates the problem of meta-coupling. In order to improve the robustness of the model, adversarial training is further introduced. The experimental results of our approach on the DuEE-Fin and CCKS2021 datasets are significantly better than the existing methods. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Extraction

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Chinese Frame Disambiguation Method Based on GCN and Gate Mechanism 21

Chinese Frame Disambiguation Method Based on GCN and Gate Me...

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21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： You, Yanan Li, Ru Su, Xuefeng Yan, Zhichao Sun, Minshuai Wang, Chao School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China School of Modern Logistics Shanxi Vocational University of Engineering Science and Technology Shanxi Jinzhong030609 China

Chinese frame disambiguation aims to select a frame that matches its semantic scene for the target word in the sentence among the candidate *** current research methods have the defects that the calculation of the hidden layer vector has nothing to do with the target word, and ignores the influence of the syntactic structure information on the frame disambiguation. Aiming at the above problems, GCN is used to model the syntactic structure information;a gate mechanism is introduced to filter the noise information irrelevant to the target word in the hidden layer vector;and on this basis, a constraint mechanism is proposed to constrain the learning of the model and improve the representation vector. The model outperforms the current state-of-the-art models on the CFN, FN1.5 and FN1.7 datasets, proving the effectiveness of the method. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Semantics

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Prompt Enhanced Generative MRC Framework for Pancreatic Cancer NER

Prompt Enhanced Generative MRC Framework for Pancreatic Canc...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Tan, ZhenDong Yang, Yan Li, Bo Wang, Beilei Jin, Gang Chen, Chengcai He, Liang East China Normal University School of Computer Science and Technology China Shanghai Key Laboratory of Multidimensional Information Processing China Navy Medical University Changhai Hospital Department of Hepatobiliary Pancreatic Surgery China Naval Medical Center of People's Liberation Army Department of Hepatobiliary Pancreatic Surgery China Naval Medical Center of People's Liberation Army Department of Marine Biomedicine and Polar Medicine China Xiaoi Research Xiaoi Robot Technology Co.Ltd China

ISBN: (纸本)9781665468190

Medical Named Entity Recognition (NER) is a fundamental but challenging task due to the lack of specialized entity datasets like tumor entities, which are often overlapped and discontinuous. In this paper, we propose a novel Prompt Enhanced Generative Machine Reading Comprehension Framework (PGMRC) to improve the overlapped and discontinuous NER performance. Specifically, we formulate NER as a Machine Reading Comprehension (MRC) task and employ a pre-trained encoder-decoder module to generate entity span sequences according to their entity query. In this way, we adopt query to guide the model to focus on answer entities in context, which can naturally solve entity overlap and alleviate the exposure bias of the generative model. Then, we introduce continuous prompts to the self-attention mechanism in Transformer to reduce the dependence on manually constructed queries. In addition, we annotate 875 pathological documents of pancreatic cancer and construct a Chinese pathological NER dataset (PAN) containing overlapped and discontinuous entities. Finally, we conduct our experiments on three widely used benchmarks (GENIA, ACE04, ACE05) and our dataset PAN. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods. © 2022 IEEE.

关键词： Diseases

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Multiple Feature Fusion-based Video Face Tracking for IoT Big Data

arXiv

引用

arXiv 2021年

作者： Li, Tianping Liu, Zhifeng Qiao, Jianping Key Laboratory of Medical Physics and Image Processing in Shandong Province Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University School of Physics and Electronics Jinan Shandong China School of Physics and Electronics Shandong Normal University JinanShandong China School of Physics and Electronics Shandong Normal University Jinan China

With the advancement of IoT and artificial intelligence technologies, and the need for rapid application growth in fields such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. In this paper, we propose a multi-feature fusion algorithm based on integral histograms and a real-time update tracking particle filtering module. First, edge and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the particle filtering algorithm to simplify the calculation steps of complex particles. Finally, the tracking window size is adjusted in real time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti-interference ability and robustness. © 2021, CC BY.

关键词： Monte Carlo methods

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Salient object detection with lossless feature reflection and weighted structural loss

arXiv

引用

arXiv 2019年

作者： Zhang, Pingping Liu, Wei Lu, Huchuan Shen, Chunhua School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Key Laboratory of Ministry of Education for System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China School of Computer Science University of Adelaide AdelaideSA50005 Australia

Salient object detection (SOD), which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to effectively learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new weighted structural loss function to ensure clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods with a large margin. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Stable Attribute Group Editing for Reliable Few-shot Image Generation

arXiv

引用

arXiv 2023年

作者： Ding, Guanqi Han, Xinzhe Wang, Shuhui Wu, Shuzhe Jin, Xin Tu, Dandan Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China Huawei Cloud EI Innovation Lab China

Few-shot image generation aims to generate data of an unseen category based on only a few samples. Apart from basic content generation, a bunch of downstream applications hopefully benefit from this task, such as low-data detection and few-shot classification. To achieve this goal, the generated images should guarantee category retention for classification beyond the visual quality and diversity. In our preliminary work, we present an "editing-based" framework Attribute Group Editing (AGE) for reliable few-shot image generation, which largely improves the performance compared with existing methods that require re-training a GAN with limited data. Nevertheless, AGE's performance on downstream classification is not as satisfactory as expected. This paper investigates the class inconsistency problem and proposes Stable Attribute Group Editing (SAGE) for more stable class-relevant image generation. Different from AGE which directly edits from a one-shot image, SAGE takes use of all given few-shot images and estimates a class center embedding based on the category-relevant attribute dictionary. Meanwhile, according to the projection weights on the category-relevant attribute dictionary, we can select category-irrelevant attributes from the similar seen categories. Consequently, SAGE injects the whole distribution of the novel class into StyleGAN's latent space, thus largely remains the category retention and stability of the generated images. Going one step further, we find that class inconsistency is a common problem in GAN-generated images for downstream classification. Even though the generated images look photo-realistic and requires no category-relevant editing, they are usually of limited help for downstream classification. We systematically discuss this issue from both the generative model and classification model perspectives, and propose to boost the downstream classification performance of SAGE by enhancing the pixel and frequency components. Extensive experime

关键词： Generative adversarial networks

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