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- MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification363
This work proposes a novel HSI classification model based on a Mamba model, named MambaHSI, which can simultaneously model long-range interaction of the whole image and integrate spatial and spectral information in an adaptive manner, and is the first image-level HSI classification model based on the Mamba.
- Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction250
TCCA is developed, which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views, and proves that the main problem of multi-view canonical correlation maximization is equivalent to finding the best rank-1 approximation of the data covariance Tensor.
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- Multiview Matrix Completion for Multilabel Image Classification160
Experimental evaluation on two real-world data sets demonstrate the effectiveness of MVMC for transductive (semisupervised) multilabel image classification, and show that MVMC can exploit complementary properties of different features and output-consistent labels for improved multilabe image classification.
- Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification154
This work introduces multiview vector-valued manifold regularization (MV3MR), which exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifoldRegularization.
- Low-Rank Multi-View Learning in Matrix Completion for Multi-Label Image Classification139
This work presents a novel multi-view learning model for MC-based image classification, called low-rank multi- view matrix completion (lrMMC), which first seeks a low-dimensional common representation of all views by utilizing the proposed low- rank multi-View learning (lrVVL) algorithm.
- Decomposition-Based Transfer Distance Metric Learning for Image Classification118
This paper considers the transfer learning setting by exploiting the large quantity of side information from certain related, but different source tasks to help with target metric learning (with only a little side information), and proposes a decomposition-based transfer DML (DTDML).
- Learning from models beyond fine-tuning114
This Review provides a comprehensive overview of current methods based on foundation models from the perspective of LFM, and emphasizes that maximizing the use of parametric knowledge in data-scarce scenarios is critical to LFM.
- Towards understanding the power of quantum kernels in the NISQ era106
This work proves that the advantage of quantum kernels is vanished for large size of datasets, few number of measurements, and large system noise and provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.
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- Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification106
A novel large margin multi- modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification that utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality.
- Cost-Sensitive Feature Selection by Optimizing F-Measures105
An effective feature selection algorithm that explores the class imbalance issue by optimizing F-measures and selects features corresponding to the best F-measure, which will fully represent the properties of all classes.
- Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain94
This paper examines the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in the target DML, and develops novel algorithm to learn either linear or nonlinear target metric.
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- Parameter Efficient Multi-task Model Fusion with Partial Linearization79
This work proposes a novel method to improve multi-task fusion for parameter-efficient fine-tuning techniques like LoRA fine-tuning, which partially linearizes only the adapter modules and applies task arithmetic over the linearized adapters.
- A fault diagnosis method based on one-dimensional data enhancement and convolutional neural network72
A fault diagnosis method based on one-dimensional data enhancement and convolutional neural network (CNN) is proposed that can improve the training accuracy and the test accuracy under the premise of ensuring the diagnosis efficiency when there are many fault types and few training data in the experimental data set.
- DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices67
This work proposes a collaborative inference framework termed DeViT to facilitate edge deployment by decomposing large ViTs, and designs a decomposition-and-ensemble algorithm based on knowledge distillation, termed DEKD, to fuse multiple small decomposed models while dramatically reducing communication overheads.
- Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning65
A lightweight autoencoder-based method to compress the large intermediate feature of DNN, then a multi-agent hybrid proximal policy optimization (MAHPPO) algorithm to solve the optimization problem with a hybrid action space is proposed.
- WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge58
A plug-in framework named WisdoM, to leverage the contextual world knowledge induced from the large vision-language models (LVLMs) for enhanced MSA, and to reduce the noise in the context, a training-free contextual fusion mechanism is designed.
- CLNode: Curriculum Learning for Node Classification55
Experimental results on real-world networks demonstrate that CLNode is a general framework that can be combined with various GNNs to improve their accuracy and robustness.
- LGViT: Dynamic Early Exiting for Accelerating Vision Transformer53
A novel two-stage training scheme is developed, including end-to-end training and self-distillation with the backbone frozen to generate early exiting ViTs, which facilitates the fusion of global and local information extracted by the two types of heads.
- Vector-Valued Multi-View Semi-Supervsed Learning for Multi-Label Image Classification51
The proposed multi-view vector-valued manifold regularization (MV$^3$MR) methodology for image classification exploits the complementary properties of different features, and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifoldRegularization.
- CDFKD-MFS: Collaborative Data-Free Knowledge Distillation via Multi-Level Feature Sharing45
This work proposes a framework termed collaborative data-free knowledge distillation via multi-level feature sharing (CDFKD-MFS), which consists of a multi-header student module, an asymmetric adversarial data- free KD module, and an attention-based aggregation module.
- Pitch-density-based features and an SVM binary tree approach for multi-class audio classification in broadcast news45
Experiments show that the proposed one-dimensional APD and RTPD features are able to achieve comparable accuracy with popular high-dimensional features in speech/music discrimination, and the SVM-BT approach demonstrates superior performance in multi-class audio classification.
- ResumeGAN42
A novel framework is proposed that targets the same task, but integrate different types of information in a more sophisticated way and introduce adversarial learning to learn more expressive representation and the effectiveness of the framework is demonstrated by extensive experiments.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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