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Found 8 papers, 1 papers with code
Future Predictive Success-or-Failure Classification for Long-Horizon Robotic Tasks
The key idea behind the proposed method is an end-to-end approach for determining whether the action plan can complete a given task instead of manually redesigning the conditions.
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.
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.
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.
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.
