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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yuan, Zhenlong Liu, Cong Shen, Fei Li, Zhaoxin Luo, Jinguo Mao, Tianlu Wang, Zhaoqi Institute of Computing Technology Chinese Academy of Sciences China Harbin Institute of Technology Shenzhen China Nanjing University of Science and Technology Nanjing China Agricultural Information Institute Chinese Academy of Agricultural Sciences China Key Laboratory of Agricultural Big Data Ministry of Agriculture and Rural Affairs China

ISBN: (纸本)157735897X

Recently, patch deformation-based methods have demonstrated significant strength in multi-view stereo by adaptively expanding the reception field of patches to help reconstruct textureless areas. However, such methods mainly concentrate on searching for pixels without matching ambiguity (i.e., reliable pixels) when constructing deformed patches, while neglecting the deformation instability caused by unexpected edge-skipping, resulting in potential matching distortions. Addressing this, we propose MSP-MVS, a method introducing multi-granularity segmentation prior for edge-confined patch deformation. Specifically, to avoid unexpected edge-skipping, we first aggregate and further refine multi-granularity depth edges gained from Semantic-SAM as prior to guide patch deformation within depth-continuous (i.e., homogeneous) areas. Moreover, to address attention imbalance caused by edge-confined patch deformation, we implement adaptive equidistribution and disassemble-clustering of correlative reliable pixels (i.e., anchors), thereby promoting attention-consistent patch deformation. Finally, to prevent deformed patches from falling into local-minimum matching costs caused by the fixed sampling pattern, we introduce disparity-sampling synergistic 3D optimization to help identify global-minimum matching costs. Evaluations on ETH3D and Tanks & Temples benchmarks prove our method obtains state-of-the-art performance with remarkable generalization. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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bigeoDepth: Leveraging Bi-Geometric Priors for Unsupervised Monocular Depth Estimation in Indoor Environments

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IEEE Transactions on Consumer Electronics 2025年

作者： Dong, Li Ren, Qingji Shi, Jianyang Zhang, Bowen Shenzhen Technology University College of Big Data and Internet Shenzhen518055 China Harbin Institute of Technology Department of Computer Science Shenzhen518055 China

Monocular depth estimation for indoor scenes remains challenging, particularly in textureless regions such as walls, floors, and ceilings, where photometric reconstruction losses provide weak supervision. Existing unsupervised approaches often fail to fully exploit the geometric regularities of indoor environments. To address these limitations, we propose bigeoDepth, an unsupervised depth estimation framework that leverages comprehensive geometric constraints. By fully integrating planar and linear structures, we introduce a novel bi-consistent loss that ensures the correctness of planes and lines in 3D space and enforces their alignment with the principal orthogonal axes under the Manhattan-world assumption. This approach provides stronger supervision signals beyond photometric loss, leading to more accurate depth predictions. Extensive experiments on the NYU Depth V2 dataset demonstrate that bigeoDepth significantly outperforms existing methods, reducing absolute relative error, root mean squared error, and logarithmic root mean squared error by 0.007, 0.017, and 0.003, respectively, compared to PLNet. Additionally, validation on the ScanNet dataset shows the generalization capability of bigeoDepth, making it applicable to a wide variety of indoor scenes. © 1975-2011 IEEE.

关键词： Mean square error

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TF-DTA: A Deep Learning Approach Using Transformer Encoder to Predict Drug-Target Binding Affinity

TF-DTA: A Deep Learning Approach Using Transformer Encoder t...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Wenjun Zhou, Yiqiang Tang, Xiwei Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Hunan Changsha China Hunan First Normal University School of Computer Science Hunan Changsha China

ISBN: (纸本)9798350337488

Bioinformatics is a rapidly growing field that involves the application of computational methods to analyze and interpret biological data. One important task in bioinformatics is predicting the drug-target affinity (DTA), which plays a significant role in drug discovery through virtual screening. Effectively predicting the association between drug molecules and target molecules can speed up the drug discovery process. The DTA can be quantitatively measured. This quantifiable affinity is more precise than a simple binary relationship. In this study, we propose a deep learning model for DTA prediction that utilizes the encoder module of the Transformer architecture. Our proposed model utilizes Convolutional Neural Networks (CNNs) and the encoder module of Transformer to characterize protein and drug sequences. The model outperforms some methods such as KronRLS, SimBoost and DeepDTA as evidenced by superior evaluation metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the Mean Index (rm2). These results demonstrate the effectiveness of the Transformer's encoder in extracting meaningful representations from sequences, thereby improving the accuracy of DTA prediction. Deep learning models for DTA prediction can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, the use of machine learning technology enables more effective and efficient drug discovery. © 2023 IEEE.

关键词： deep learning drug-target affinity multi-head attention mechanism

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A Study on the Pricing of Used Sailboats: Utilizing Random Forest Models to Analyze the Impact of Multiple Factors 3

A Study on the Pricing of Used Sailboats: Utilizing Random F...

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3rd IEEE International Conference on Electrical Engineering, big data and Algorithms, EEBDA 2024

作者： Zhou, Huimin Qian, Jiajun Sun, Feifan School of Mathematics and Big Data Chongqing University of Arts and Sciences Chongqing China Suzhou University of Science and Technology Faculty of Electronic and Information Engineering Suzhou China College of Economics and Management Northeast Forestry University Harbin China

ISBN: (纸本)9798350380989

This study explores the impact of various factors on the valuation of sailboats. The research delves into the mathematical correlation between the prices of secondhand sailboats and 11 specific factors. Initially, any missing values were removed, outliers were substituted, and box plots were utilized to validate the precision of the sample data. Furthermore, five supplementary parameters were sourced from public websites, encompassing displacement, draft, beam, material, and GDP. Subsequently, after organizing the data, categorical variables were encoded using sequence and label encoding. Following this, multiple linear regression models, ridge regression models, and random forest models were constructed using the entire dataset. Through a comparative analysis of the three models' accuracy, the random forest model was chosen as the ultimate model for elucidating the pricing of each sailboat. The predictive precision of the random forest model was scrutinized, yielding a prediction accuracy of R2=0.786. © 2024 IEEE.

关键词： Mathematical relationship Multiple linear regression models Random forest model Sailboats

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FallDeWideo: Vision-Aided Wireless Sensing dataset for Fall Detection with Commodity Wi-Fi Devices 3

FallDeWideo: Vision-Aided Wireless Sensing Dataset for Fall ...

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3rd ACM MobiCom Workshop on Integrated Sensing and Communication Systems, ISACom 2023

作者： Cai, Zhijie Chen, Tingwei Zhou, Fujia Cui, Yuanhao Li, Hang Li, Xiaoyang Zhu, Guangxu Shi, Qingjiang The Chinese University of Hong Kong Shenzhen Shenzhen China Tongji University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Southern University of Science and Technology China

ISBN: (纸本)9798400703645

Falling is one of the most dangerous events for the elderly and one of the most pressing public concern, which calls for an accurate, efficient, ubiquitous, cost-effective and privacy-preserving fall detection system to mitigate the negative consequences. Wi-Fi sensing-based method is considered as a potential technique to meet such needs. Existing solutions simply treat fall detection simply as a binary classification task, which leads to interpretability risks. To address this issue, we present FallDeWideo, the first multi-modal dataset dedicated to fall detection, comprising Wi-Fi CSI data and videos recorded during various kinds of events. We provide benchmark model for this dataset as well. Specifically, we train a CSI-based human pose estimation model (HPE) using video data as the supervision modality. The trained model can estimate human pose solely on Wi-Fi channel state information (CSI), and then detects whether a fall event occurs. This pipeline extracts rich information from CSI data and detects fall with more than just an alarm, but attached with an HPE, which allows more refined management of fall risks. We envision that this dataset will contribute to the wireless sensing research coomunity with respect to healthcare, action recognition, and cross-modal sensing. Codes and link to the dataset is available at https://***/shawnnn3di/falldewideo. © 2023 ACM.

关键词： Wi-Fi

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In-situ synthesized SiC/TiC/SiTiOC hybrid nanofiber mats for broad-range temperature microwave absorption

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science China Technological sciences 2025年第6期68卷 183-196页

作者： Yashan HUO Yuetong QIAN Minshan ZHAO Lei ZHANG Yujia TAN Zhihui HE Peng MIAO Fuchun ZHANG Wei CUI Renchao CHE School of Physics and Electronic Information Yan'an University Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Laboratory of Advanced Materials Shanghai Key Lab of Molecular Catalysis and Innovative Materials Academy for Engineering &Technology Advanced Coatings Research Center of Ministry of Education of China Fudan University School of Materials and Chemical Engineering Xi'an Technological University School of Materials Science & Engineering Tongji University College of Physics Donghua University

Efficient, temperature-insensitive electromagnetic attenuation materials are critical for high microwave absorption at high temperatures. This study presents SiC/TiC/SiTiOC hybrid nanofiber mats constructed using an in-situ synthesis method;these mats incorporate SiC/TiC as the microwave loss unit and SiTiOC as the antioxidant unit, offering an innovative approach to high-temperature microwave absorption. The introduction of high-conductivity TiC not only optimizes the dielectric constant and impedance matching of the material but also enhances its thermal cycling stability. This design ensures steady electromagnetic attenuation, enabling the hybrid nanofiber mats to maintain robust absorption performance in the X-band, even at high temperatures. At 873 K, the material demonstrates an effective absorption bandwidth of 3.6 GHz and a minimum reflection loss of-45.55 dB. Compared to single-system absorbers, such as SiC/C nanofiber materials, with a single system, the multicomponent SiC/TiC/SiTiOC hybrid nanofiber mats deliver reliable absorption performance in the temperature range of 293–873 K. This study confirms that highly conductive titanium-based ceramic materials used for dielectric property regulation effectively address the challenges of poor temperature stability and limited reusability in high-temperature carbon-ceramic absorbing materials, providing valuable insights and guidance for designing efficient microwave absorbers that can operate across a wide temperature range.

关键词： electrospinning SiC/TiC/SiTiOC hybrid nanofiber mats high-temperature microwave absorption dielectric properties thermal stability

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MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering

MatchPrompt: Prompt-based Open Relation Extraction with Sema...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wang, Jiaxin Zhang, Lingling Liu, Jun Liang, Xi Zhong, Yujie Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University China Lenovo Research Beijing China

Relation clustering is a general approach for open relation extraction (OpenRE). Current methods have two major problems. One is that their good performance relies on large amounts of labeled and pre-defined relational instances for pre-training, which are costly to acquire in reality. The other is that they only focus on learning a high-dimensional metric space to measure the similarity of novel relations and ignore the specific relational representations of clusters. In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few predefined relational instances as well as mine the specific meanings for cluster interpretability. To our best knowledge, we are the first to introduce a prompt-based framework for unlabeled clustering. Experimental results on different datasets show that MatchPrompt achieves the new SOTA results for OpenRE. © 2022 Association for Computational Linguistics.

关键词： Extraction

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A Framework based on Deep Neural Network for Ranking-oriented Software Defect Prediction 23

A Framework based on Deep Neural Network for Ranking-oriente...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Dai, Jiapeng Yang, Xiaoxing Huang, Bingding Lu, Xiaofen Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350319583

Software systems are getting larger and more complex than ever before. In order to improve software reliability, software defect prediction is applied to assist developers in bug discovery. The ranking-oriented software defect prediction aims to rank software modules according to the predicted defect counts. However, existing ranking-oriented defect prediction models are constructed based on traditional hand-crafted features, which might overlook the rich syntactic information buried inside the source codes. In this paper, we propose a universal deep learning-based framework called United Deep Network for ranking-oriented software defect prediction. This framework utilizes deep neural networks to automatically generate features from source code with the syntactic and structural information preserved, and it can combine extracted features with traditional hand-crafted features in order to take advantage of both kinds of features to construct prediction models. Experimental results over 29 sets of data show the good performance of the proposed framework for building ranking-oriented defect prediction models. © 2023 IEEE.

关键词： Deep neural networks

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LGMD-based Neural Network for Detecting Abnormal Velocity Targets in Moving Crowd 9

LGMD-based Neural Network for Detecting Abnormal Velocity Ta...

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9th International Symposium on Computer and Information Processing technology, ISCIPT 2024

作者： Zhao, Zhongxiang Hu, Bin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University College of Computer Science & Technology Department of Computer Science Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (纸本)9798350388404

Detecting abnormal velocity targets is critical especially in public surveillance for crowd activity monitoring. Although there are some explorations on one such issue, traditional methods still perform poorly in complex scenes. In this paper, a novel visual neural network is investigated to perceive abnormal velocity targets in moving crowd, based on the latest neurophysiological achievements revealed in locusts' vision systems. The proposed neural network contains two neural counterparts, i.e., the presynaptic and the postsynaptic networks. The former one receives visual signals and processes them to capture the motion cues of different targets, and the latter one filters the salience energies to perceive abnormal velocity targets in the field of view. Numerical experiments carried out show that the proposed neural network can effectively detect abnormal velocity targets in moving crowd. This study is an important step towards dynamic visual information processing in crowd behavior analysis. © 2024 IEEE.

关键词： Visual servoing

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Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension 39

Hierarchical Alignment-enhanced Adaptive Grounding Network f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yaxian Ding, Henghui He, Shuting Jiang, Xudong Wei, Bifan Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University China Institute of Big Data Fudan University China Shanghai University of Finance and Economics China Nanyang Technological University Singapore School of Continuing Education Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University China

ISBN: (纸本)157735897X

In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing no-target and multi-target expressions. Existing REC methods face challenges in handling the complex cases encountered in GREC, primarily due to their fixed output and limitations in multi-modal representations. To address these issues, we propose a Hierarchical Alignment-enhanced Adaptive Grounding Network (HieA2G) for GREC, which can flexibly deal with various types of referring expressions. First, a Hierarchical Multi-modal Semantic Alignment (HMSA) module is proposed to incorporate three levels of alignments, including word-object, phrase-object, and text-image alignment. It enables hierarchical cross-modal interactions across multiple levels to achieve comprehensive and robust multi-modal understanding, greatly enhancing grounding ability for complex cases. Then, to address the varying number of target objects in GREC, we introduce an Adaptive Grounding Counter (AGC) to dynamically determine the number of output targets. Additionally, an auxiliary contrastive loss is employed in AGC to enhance object-counting ability by pulling in multi-modal features with the same counting and pushing away those with different counting. Extensive experimental results show that HieA2G achieves new state-of-the-art performance on the challenging GREC task and also the other 4 tasks, including REC, Phrase Grounding, Referring Expression Segmentation (RES), and Generalized Referring Expression Segmentation (GRES), demonstrating the remarkable superiority and generalizability of the proposed HieA2G. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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