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Found 6 papers, 0 papers with code
A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization
Motivated by these issues, this paper proposes a novel compositional feature embedding and similarity metric (CECS).
Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization
The advantage of the proposed method is that the feature detection and extraction model only requires a small amount of target region samples with bounding boxes for training, then it can automatically locate the target area for a large number of images in the dataset at a high detection accuracy.
A Generalized Kernel Risk Sensitive Loss for Robust Two-Dimensional Singular Value Decomposition
Two-dimensional singular decomposition (2DSVD) has been widely used for image processing tasks, such as image reconstruction, classification, and clustering.
A Robust Matching Pursuit Algorithm Using Information Theoretic Learning
Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are both suboptimal when there are non-Gaussian noises or outliers in the observation data.
A Unified Weight Learning and Low-Rank Regression Model for Robust Complex Error Modeling
For the structured error caused by occlusions or disguises, we propose a GC function based rank approximation to measure the rank of error matrices.
Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy
Traditional tensor decomposition methods, e. g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers.
