most citedAnomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks

206 citations · 270 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20211 cited

Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization

Navya Nagananda, Breton Minnehan, Andreas Savakis

Linear Discriminant Analysis (LDA) is commonly used for dimensionality reduction in pattern recognition and statistics. It is a supervised method that aims to find the most discrim…

cs.CV202112 cited

ConDA: Continual Unsupervised Domain Adaptation

Abu Md Niamul Taufique, Chowdhury Sadman Jahan, Andreas Savakis

Domain Adaptation (DA) techniques are important for overcoming the domain shift between the source domain used for training and the target domain where testing takes place. However…

cs.CV202115 cited

Benchmarking Deep Trackers on Aerial Videos

Abu Md Niamul Taufique, Breton Minnehan, Andreas Savakis

In recent years, deep learning-based visual object trackers have achieved state-of-the-art performance on several visual object tracking benchmarks. However, most tracking benchmar…

cs.CV20215 cited

Visualization of Deep Transfer Learning In SAR Imagery

Abu Md Niamul Taufique, Navya Nagananda, Andreas Savakis

Synthetic Aperture Radar (SAR) imagery has diverse applications in land and marine surveillance. Unlike electro-optical (EO) systems, these systems are not affected by weather cond…

cs.CV202131 cited

OmniPose: A Multi-Scale Framework for Multi-Person Pose Estimation

Bruno Artacho, Andreas Savakis

We propose OmniPose, a single-pass, end-to-end trainable framework, that achieves state-of-the-art results for multi-person pose estimation. Using a novel waterfall module, the Omn…

cs.CV2016206 cited

Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks

Jefferson Ryan Medel, Andreas Savakis

Automating the detection of anomalous events within long video sequences is challenging due to the ambiguity of how such events are defined. We approach the problem by learning gen…