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Automatic Weight Allocation: optimizing remote sensing image retrieval from contrastive learning perspective

Multimedia Systems 2025年第3期31卷 1-19页

作者： Wang, Sijia Ge, Yun Liu, Qiyang Zeng, Yan School of Software Nanchang Hangkong University Nanchang China Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition Nanchang China

Traditional supervised learning methods achieve remarkable performance in high-resolution remote sensing image retrieval, but are limited by the dependence on large-scale annotated images. Contrastive learning can leverage unlabeled images to learn powerful visual features, demonstrating its potential in many unsupervised tasks. Moreover, hash algorithms show significant potential in the field of image retrieval with their advantages in efficiency and storage. Therefore, we propose the Contrastive Hashing Framework based on Automatic Weight Allocation. The framework employs a two-stage training strategy. In the feature learning stage, we propose the Automatic Weighted Contrastive Loss (AWCLoss). It incorporates Gaussian weighting and dynamic adjustment strategies to improve loss functions, enabling them to focus on the distinctiveness and importance of samples. Gaussian weighting assigns different weight values based on the similarity of sample pairs, enhancing the learning of critical sample pairs. Meanwhile, the dynamic adjustment strategy sets a threshold to identify hard negative samples and then adjusts the weight values to weaken the model from being disturbed by hard negative samples. In the hashing learning stage, a hashing layer is added to the end of the network, which converts high-dimensional representations into hash codes. The Quantization loss is introduced to learn the hash codes so that the semantic similarity structure between data can be preserved in hamming space. Additionally, the AWCLoss is utilized to enhance the discriminative power of the hash codes. Extensive experiments on three remotely sensed datasets UCM, AID and NWPU-RESISC45 have demonstrated the significant superiority of our approach in remote sensing image retrieval. Our source code is available at https://***/WANGSJ77/AWCH .

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DFL: cross-view cross-layer discriminative feature learning for fine-grained 3D shape classification

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Neural Computing and Applications 2025年 1-22页

作者： Jiang, Jinzhe Bai, Jing Ma, Xiangyu The School of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images Processing and Pattern Recognition Laboratory North Minzu University Yinchuan China

Fine-grained 3D shape classification poses challenges in effectively capturing and integrating discriminative features residing in subtle local regions. Previous methods typically extract features independently from individual views of 3D shapes, with a focus on various strategies for fusing these extracted view features. However, this approach neglects interview correlations and potential redundancies among different views. In this study, we introduce $$\hbox {C}^2$$ DFL, which consists of two primary modules: cross-view discriminative feature extraction (CV-DFE) and cross-layer discriminative feature fusion (CL-DFF). CV-DFE integrates discriminative features by merging inputs from multiple views, mitigating limitations associated with isolated feature extraction. CL-DFF dynamically selects key tokens using a transformer model to interactively fuse discriminative features from various levels. Extensive experiments conducted on three categories of the FG3D dataset demonstrate the exceptional efficacy of $$\hbox {C}^2$$ DFL in capturing and integrating discriminative features of 3D shapes. The proposed method achieves state-of-the-art accuracy in fine-grained 3D shape classification (FGSC).

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FG3DFormer: Fine-Grained 3D Shape Classification Based on Vision Transformer

FG3DFormer: Fine-Grained 3D Shape Classification Based on Vi...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiangyu Ma Jing Bai Jinzhe Jiang Bin Peng The School of Computer Science and Engineering North Minzu University The Key Laboratory of Images Processing and Pattern Recognition Laboratory Yinchuan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Fine-grained 3D shape classification (FGSC) remains challenging due to the difficulty of adaptively capturing global structure differences and subtle inter-class distinctions. This paper directly extends Vision Transformer (ViT) to FGSC, proposing a pure Transformer network FG3DFormer that fully leverages ViT’s global correlation and local attention abilities. FG3Dformer comprises the Hierarchical Feature Extraction (HFE) and the Hierarchical Feature Refinement (HFR), interconnected through the Adaptive View Region Selection (AVRS). Firstly, the HFE comprehensively evaluates the significance of intra-view patches and views driven by inter-view and intraview attention. Then, the AVRS adaptively selects crucial patch Tokens from different views to serve as sources of subtle local features. Finally, the HFR refines the 3D shape descriptor, capturing more discriminative global and subtle local features by leveraging both the view and selected crucial patch Tokens. Extensive experiments on FG3D and ModelNet40 demonstrate the superiority of FG3Dformer in FGSC and meta-category 3D shape classification tasks.

关键词： Computer vision Visualization Solid modeling Three-dimensional displays Correlation Shape Signal processing Transformers Feature extraction Speech processing

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C2DFL: cross-view cross-layer discriminative feature learning for fine-grained 3D shape classification

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Neural Computing and Applications 2025年

作者： Jiang, Jinzhe Bai, Jing Ma, Xiangyu The School of Computer Science and Engineering North Minzu University Yinchuan750021 China The Key Laboratory of Images Processing and Pattern Recognition Laboratory North Minzu University Yinchuan750021 China

Fine-grained 3D shape classification poses challenges in effectively capturing and integrating discriminative features residing in subtle local regions. Previous methods typically extract features independently from individual views of 3D shapes, with a focus on various strategies for fusing these extracted view features. However, this approach neglects interview correlations and potential redundancies among different views. In this study, we introduce C2DFL, which consists of two primary modules: cross-view discriminative feature extraction (CV-DFE) and cross-layer discriminative feature fusion (CL-DFF). CV-DFE integrates discriminative features by merging inputs from multiple views, mitigating limitations associated with isolated feature extraction. CL-DFF dynamically selects key tokens using a transformer model to interactively fuse discriminative features from various levels. Extensive experiments conducted on three categories of the FG3D dataset demonstrate the exceptional efficacy of C2DFL in capturing and integrating discriminative features of 3D shapes. The proposed method achieves state-of-the-art accuracy in fine-grained 3D shape classification (FGSC). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： 3D shape classification Cross-layer feature fusion Cross-view attention Fine-grained classification Token selection Transformer

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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DLS-HCAN: Duplex Label Smoothing Based Hierarchical Context-Aware Network for Fine-grained 3D Shape Classification

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IEEE Transactions on Multimedia 2025年

作者： Bai, Shaojin Zheng, Liang Bai, Jing Ma, Xiangyu North Minzu University School of Computer Science and Engineering Yinchuan750021 China Liupanshan Laboratory Yinchuan750021 China North Minzu University Key Laboratory of Images Processing and Pattern Recognition LaboratoryCommission: IPPRLab Yinchuan750021 China

Fine-grained 3D shape classification (FGSC) has garnered significant attention recently and has made notable advancements. However, due to high inter-class similarity and intra-class diversity, it is still a challenge for existing methods to capture subtle differences between different subcategories for FGSC. On the one hand, one-hot labels in loss function are too hard to describe the above data characteristics, and on the other hand, local details are submerged in the global features extraction process and final network constraints, impacting classification results. In this paper, we propose a duplex label smoothingbased hierarchical context-aware network for fine-grained 3D shape classification, named DLS-HCAN. Specifically, DLS-HCAN firstly employs a hierarchical context-aware network (HCAN), in which the intra-view context attention mechanism (intra-ATT) and the inter-view context multilayer perceptron (inter-MLP) are designed to focus on and discern the beneficial local details. Subsequently, we propose a novel duplex label smoothing (DLS) regularization in which shape-level and view-level smooth labels are separately applied in two improved loss functions, adapting to the fine-grained data characteristics and considering the varying uniqueness of different views. Notably, our approach does not require additional annotation information. Experimental results and comparison with state-of-the-art methods demonstrate the superiority of our proposed DLS-HCAN for FGSC. In addition, our approach also achieves comparable performance for the coarse-grained dataset on ModelNet40. © 2025 IEEE.

关键词： Coarse-grained modeling

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DeepRAG: Thinking to Retrieval Step by Step for Large Language Models

arXiv

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arXiv 2025年

作者： Guan, Xinyan Zeng, Jiali Meng, Fandong Xin, Chunlei Lu, Yaojie Lin, Hongyu Han, Xianpei Sun, Le Zhou, Jie Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Pattern Recognition Center WeChat AI Tencent Inc China

Large Language Models (LLMs) have shown remarkable potential in reasoning while they still suffer from severe factual hallucinations due to timeliness, accuracy, and coverage of parametric knowledge. Meanwhile, integrating reasoning with retrieval-augmented generation (RAG) remains challenging due to ineffective task decomposition and redundant retrieval, which can introduce noise and degrade response quality. In this paper, we propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP), enabling strategic and adaptive retrieval. By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step. Experiments show that DeepRAG improves retrieval efficiency while improving answer accuracy by 21.99%, demonstrating its effectiveness in optimizing retrieval-augmented reasoning. © 2025, CC BY.

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Reflecting topology consistency and abnormality via learnable attentions for airway labeling

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International Journal of Computer Assisted Radiology and Surgery 2025年 1-9页

作者： Li, Chenyu Zhang, Minghui Zhang, Chuyan Gu, Yun Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Flexible Medical Robotics Tongren Hospital Shanghai Jiao Tong University Shanghai China

Purpose: Accurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway labeling is challenging due to significant anatomical variations. Previous methods are prone to generate inconsistent predictions, hindering preoperative planning and intraoperative navigation. This paper aims to enhance topological consistency and improve the detection of abnormal airway branches. Methods: We propose a transformer-based framework incorporating two modules: the soft subtree consistency (SSC) and the abnormal branch saliency (ABS). The SSC module constructs a soft subtree to capture clinically relevant topological relationships, allowing for flexible feature aggregation within and across subtrees. The ABS module facilitates interaction between node features and prototypes to distinguish abnormal branches, preventing the erroneous features aggregation between normal and abnormal nodes. Results: Evaluated on a challenging dataset characterized by severe airway deformities, our method achieves superior performance compared to state-of-the-art approaches. Specifically, it attains an 83.7% subsegmental accuracy, along with a 3.1% increase in segmental subtree consistency, a 45.2% increase in abnormal branch recall. Notably, the method demonstrates robust performance in cases with airway deformities, ensuring consistent and accurate labeling. Conclusion: The enhanced topological consistency and robust identification of abnormal branches provided by our method offer an accurate and robust solution for airway labeling, with potential to improve the precision and safety of bronchoscopy procedures. © CARS 2025.

关键词： Airway anatomical labeling Anomaly detection Structural prior Transformer

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Thermal-Torsion Coupling Loading Analysis and Optimization of 3D PoP Assembly Solder Joints at Different Temperatures

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Gao, Chao Huang, Chunyue Liang, Yin Dian, Chen Lan, Jingyi Wang, Gui Guilin University of Electronic Technology School of Electrical and Mechanical Engineering Guilin541004 China Chengdu University Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu610106 China Chengdu Aeronautic Vocational and Technical College Chengdu610021 China

A simulation analysis was conducted to investigate the stress distribution of Package-on-Package (PoP) assembly array solder joints subjected to thermal-torsional coupling loads, with the validity of the simulation confirmed through coupled loading stress-strain measurement experiments. The volume-weighted average method was employed to calculate the average equivalent stress (Δσ) of the critical solder joint danger element layer within the PoP assembly array, using Δσ as the evaluation benchmark. The impact and sensitivity of structural parameters on the thermal-torsional coupling stress of solder joints were examined. A structural parameter sensitivity model was developed to facilitate the stability and optimization analysis of the structural parameter intervals. The results indicate that solder joints in the lower array of the PoP assembly experience higher thermal-torsional coupling stress, with the peak internal stress occurring at the interface between the solder joint and the copper pad. The variation in thermal-torsional coupling stress of the array solder joints is more pronounced in the low-temperature region compared to the high-temperature region, with maximum stress occurring at -55°C and minimum stress observed at 125°C. Within the structural parameter range, the Δσ value of the critical solder joint shows a negative correlation with solder joint height, solder joint diameter, and substrate thickness, and a positive correlation with PCB thickness. The sensitivity to structural parameters follows the order: solder joint diameter, solder joint height, PCB thickness, and substrate thickness. Finally, the optimal range of structural parameters was determined, and the stability of this range was assessed. © 2011-2012 IEEE.

关键词： Sensitivity analysis

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TNIE-SLAM: Neural Implicit Surface Reconstruction for Tracking-Oriented SLAM

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Journal of Intelligent & Robotic Systems 2025年第2期111卷 1-22页

作者： Gan, Baolin Zhang, Congxuan Chen, Shuaixin Chen, Zhen He, Chao Lu, Ke Lu, Feng School of Instrument Science and Optoelectronic Engineering Nanchang Hangkong University Nanchang China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Information Engineering Nanchang Hangkong University Nanchang China The College of Engineering Science University of Chinese Academy of Sciences Beijing China

Recent studies on simultaneous localization and mapping (SLAM) have tended to employ implicit neural representation, which can improve the efficiency and robustness of SLAM system. However, these methodologies still face challenges, such as tracking failures and low-precision mapping. In this paper, we propose a dense reconstruction visual SLAM system enhanced with closed-loop threading and local map optimization, named TNIE-SLAM. First, we propose a tracking module that utilizes the similarity of ORB feature descriptors and the feature overlap rate of the current frame to model key frames, and then we define a complete and accurate initial map based on full bundle adjustment, which addresses the issue of tracking failure due to undermapped areas. Second, we add the 2D features of the initial map to the spatiotemporal encoding module to obtain the 3D features, enabling real-time prediction and tracking of unknown areas. Finally, considering the low-precision mapping issue arising from the complex geometric shapes of objects within the scene, we propose a local map optimization module that utilizes truncated signed distance fields to model 3D features and update the spatial occupancy of boundary and contour features of objects. We test our method on the synthetic Replica dataset and the real-world ScanNet and TUM RGB-D datasets to compare with some state-of-the-art RGB-D SLAM methods, and the experimental results indicate our method performs well in both tracking and mapping accuracy, surpassing the existing dense neural RGB-D SLAM methods.

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