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Found 7 papers, 1 papers with code
Degree-based stratification of nodes in Graph Neural Networks
Here, we propose a different approach that is based on a stratification of the graph nodes.
Deep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces
This paper introduces a novel classification loss that maximizes the margin in both the Euclidean and angular spaces at the same time.
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition
In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation by using the polyhedral conic classification function.
TRAT: Tracking by Attention Using Spatio-Temporal Features
Although motion provides distinctive and complementary information especially for fast moving objects, most of the recent tracking architectures primarily focus on the objects' appearance information.
Dissected 3D CNNs: Temporal Skip Connections for Efficient Online Video Processing
Convolutional Neural Networks with 3D kernels (3D-CNNs) currently achieve state-of-the-art results in video recognition tasks due to their supremacy in extracting spatiotemporal features within video frames.
Discriminatively Learned Convex Models for Set Based Face Recognition
Majority of the image set based face recognition methods use a generatively learned model for each person that is learned independently by ignoring the other persons in the gallery set.
Polyhedral Conic Classifiers for Visual Object Detection and Classification
Our experiments show that they significantly outperform both linear SVMs and existing one-class discriminants on a wide range of object detection, open set recognition and conventional closed-set classification tasks.
