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Found 14 papers, 2 papers with code
Occlusion Sensitivity Analysis with Augmentation Subspace Perturbation in Deep Feature Space
In this study, we introduce the Occlusion Sensitivity Analysis with Deep Feature Augmentation Subspace (OSA-DAS), a novel perturbation-based interpretability approach for computer vision.
Diffusion-based Holistic Texture Rectification and Synthesis
We present a novel framework for rectifying occlusions and distortions in degraded texture samples from natural images.
Controllable Multi-domain Semantic Artwork Synthesis
To address this problem, we propose a dataset, which we call ArtSem, that contains 40, 000 images of artwork from 4 different domains with their corresponding semantic label maps.
Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces
This paper proposes a new method for anomaly detection in time-series data by incorporating the concept of difference subspace into the singular spectrum analysis (SSA).
Adaptive occlusion sensitivity analysis for visually explaining video recognition networks
The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D temporal-spatial data space and then measure the change degree in the output score.
Grassmannian learning mutual subspace method for image set recognition
The image set is represented by a low-dimensional input subspace; and this input subspace is matched with reference subspaces by a similarity of their canonical angles, an interpretable and easy to compute metric.
Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition
Automatic analysis of bioacoustic signals is a fundamental tool to evaluate the vitality of our planet.
Discriminant analysis based on projection onto generalized difference subspace
To avoid the difficulty, we first introduce geometrical Fisher discriminant analysis (gFDA) based on a simplified Fisher criterion.
Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints
Planar markers are useful in robotics and computer vision for mapping and localisation.
Tensor Analysis with n-Mode Generalized Difference Subspace
In this paper, we present a new method for multi-dimensional data classification that relies on two premises: 1) multi-dimensional data are usually represented by tensors, since this brings benefits from multilinear algebra and established tensor factorization methods; and 2) multilinear data can be described by a subspace of a vector space.
Constrained Mutual Convex Cone Method for Image Set Based Recognition
Moreover, to enhance the framework, we introduce a discriminant space that maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected convex cones onto the discriminant space, similar to the Fisher discriminant analysis.
Text Classification based on Word Subspace with Term-Frequency
To measure the similarity between texts, we propose the novel concept of word subspace, which can represent the intrinsic variability of features in a set of word vectors.
A Method Based on Convex Cone Model for Image-Set Classification with CNN Features
In this paper, we propose a method for image-set classification based on convex cone models, focusing on the effectiveness of convolutional neural network (CNN) features as inputs.
