Search Results for author: Ashish Singh

Found 8 papers, 3 papers with code

EVAL: Explainable Video Anomaly Localization

no code implementations15 Dec 2022 Ashish Singh, Michael J. Jones, Erik Learned-Miller

We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes.

Anomaly Detection Video Anomaly Detection

Inv-SENnet: Invariant Self Expression Network for clustering under biased data

no code implementations13 Nov 2022 Ashutosh Singh, Ashish Singh, Aria Masoomi, Tales Imbiriba, Erik Learned-Miller, Deniz Erdogmus

Subspace clustering algorithms are used for understanding the cluster structure that explains the dataset well.

Mid infrared spectroscopy and milk quality traits: a data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2021"

1 code implementation5 Jul 2021 Maria Frizzarin, Antonio Bevilacqua, Bhaskar Dhariyal, Katarina Domijan, Federico Ferraccioli, Elena Hayes, Georgiana Ifrim, Agnieszka Konkolewska, Thach Le Nguyen, Uche Mbaka, Giovanna Ranzato, Ashish Singh, Marco Stefanucci, Alessandro Casa

A chemometric data analysis challenge has been arranged during the first edition of the "International Workshop on Spectroscopy and Chemometrics", organized by the Vistamilk SFI Research Centre and held online in April 2021.

Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management

no code implementations15 Apr 2021 Aloysius Lim, Ashish Singh, Jody Chiam, Carly Eckert, Vikas Kumar, Muhammad Aurangzeb Ahmad, Ankur Teredesai

Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature.

BIG-bench Machine Learning Diabetes Prediction +1

Automatic adaptation of object detectors to new domains using self-training

1 code implementation CVPR 2019 Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh, SouYoung Jin, Huaizu Jiang, Liangliang Cao, Erik Learned-Miller

Our results demonstrate the usefulness of incorporating hard examples obtained from tracking, the advantage of using soft-labels via distillation loss versus hard-labels, and show promising performance as a simple method for unsupervised domain adaptation of object detectors, with minimal dependence on hyper-parameters.

Knowledge Distillation Pedestrian Detection +1

Unsupervised Hard Example Mining from Videos for Improved Object Detection

no code implementations ECCV 2018 SouYoung Jin, Aruni RoyChowdhury, Huaizu Jiang, Ashish Singh, Aditya Prasad, Deep Chakraborty, Erik Learned-Miller

In this work, we show how large numbers of hard negatives can be obtained {\em automatically} by analyzing the output of a trained detector on video sequences.

Face Detection object-detection +2

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