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Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization
Linear Discriminant Analysis (LDA) is commonly used for dimensionality reduction in pattern recognition and statistics.
Benchmarking Deep Trackers on Aerial Videos
In recent years, deep learning-based visual object trackers have achieved state-of-the-art performance on several visual object tracking benchmarks.
Cascaded Projection: End-to-End Network Compression and Acceleration
We propose a data-driven approach for deep convolutional neural network compression that achieves high accuracy with high throughput and low memory requirements.
DEFRAG: Deep Euclidean Feature Representations through Adaptation on the Grassmann Manifold
We propose a novel technique for training deep networks with the objective of obtaining feature representations that exist in a Euclidean space and exhibit strong clustering behavior.
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