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- Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression589
Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually and propose a maximum a posteriori probability framework for SR recovery.
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- Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction310
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- Bayesian Tensor Approach for 3-D Face Modeling147
A decoupled probabilistic algorithm, named Bayesian tensor analysis (BTA), which can automatically and suitably determine dimensionality for different modalities of tensor data and empirical studies on expression retargeting justify the advantages of BTA.
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- Enhanced Biologically Inspired Model for Object Recognition75
An enhanced BIM (EBIM) in terms of removing uninformative inputs by imposing sparsity constraints and applying a feedback loop to middle level feature selection is developed, motivated by relevant psychophysical research findings.
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- 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.
- Grassmannian Regularized Structured Multi-View Embedding for Image Classification48
This paper proposes a novel multi-view embedding framework, termed as Grassmannian regularized structured multi- view embedding, or GrassReg, which transfers the graph Laplacian obtained from each view to a point on the Grassmann manifold and penalizes the disagreement between different views according to Grassmanian distance.
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- Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications14
A novel sparse model inversion strategy, as a plug-and-play extension to speed up existing dense inversion methods with no need for modifying their original loss functions, that selectively invert semantic foregrounds while stopping the inversion of noisy backgrounds and potential spurious correlations.
- Local-consistent Transformation Learning for Rotation-invariant Point Cloud Analysis12
Equipped with LCRF and RPR, the LocoTrans is capable of learning local-consistent transformation and preserving local geometry, which benefits rotation invariance learning.
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- On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey8
This paper thoroughly investigates recent advances in element-wise ZSIR and provides a sound basis for its future development and integrates three basic ZSIR tasks into a unified element-wise paradigm and provides a detailed taxonomy and analysis of the main approaches.
- Cross-Domain Diffusion With Progressive Alignment for Efficient Adaptive Retrieval7
This approach revisits unsupervised efficient domain adaptive retrieval from a graph diffusion perspective, simulating cross-domain adaptation dynamics to achieve a stable target domain adaptation process and enables effective domain adaptive hash learning.
- Task-Distributionally Robust Data-Free Meta-Learning6
This paper proposes a trustworthy DFML framework comprising three components: synthetic task reconstruction, meta-learning with task memory interpolation, and automatic model selection, which seamlessly incorporates an automatic model selection mechanism to automatically filter out untrustworthy models during the meta-learning process.
- Toward Understanding the Generalizability of Delayed Stochastic Gradient Descent4
This paper investigates sharper generalization error bound for SGD with asynchronous delay with upper bounds on the generalization error of <inline-formula><tex-math notation="LaTeX">$\widetilde{\mathcal {O}}(\frac{1}{n})$</tex-math><alternatives><mml:math><mml:mrow><mml:mrow><mml:mover accent="true"><mml:mi mathvariant="script
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- Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment2
This work proposes a novel learning strategy to regularize the pseudo-labels generated for training, thus effectively narrowing the gaps between pseudo-labels and model predictions and introduces an Entropy Regularization loss and a Distribution Alignment loss for label-efficient learning, resulting in an ERDA learning strategy.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. 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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