Search Results for author: Thomas Manzini

Found 8 papers, 4 papers with code

Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters

no code implementations10 May 2024 Thomas Manzini, Priyankari Perali, Raisa Karnik, Mihir Godbole, Hasnat Abdullah, Robin Murphy

This work presents the first quantitative study of alignment errors between small uncrewed aerial systems (sUAS) geospatial imagery and a priori building polygons and finds that alignment errors are non-uniform and irregular.

Improving Drone Imagery For Computer Vision/Machine Learning in Wilderness Search and Rescue

1 code implementation5 Sep 2023 Robin Murphy, Thomas Manzini

This paper describes gaps in acquisition of drone imagery that impair the use with computer vision/machine learning (CV/ML) models and makes five recommendations to maximize image suitability for CV/ML post-processing.

Open Problems in Computer Vision for Wilderness SAR and The Search for Patricia Wu-Murad

2 code implementations26 Jul 2023 Thomas Manzini, Robin Murphy

This paper details the challenges in applying two computer vision systems, an EfficientDET supervised learning model and the unsupervised RX spectral classifier, to 98. 9 GB of drone imagery from the Wu-Murad wilderness search and rescue (WSAR) effort in Japan and identifies 3 directions for future research.

Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities

2 code implementations19 Dec 2018 Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabas Poczos

Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input.

Machine Translation Multimodal Sentiment Analysis +1

Language Informed Modeling of Code-Switched Text

no code implementations WS 2018 Ch, Khyathi u, Thomas Manzini, Sumeet Singh, Alan W. black

Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities.

Language Modelling Machine Translation +1

How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing

no code implementations WS 2017 Ravich, Abhilasha er, Thomas Manzini, Matthias Grabmair, Graham Neubig, Jonathan Francis, Eric Nyberg

Wang et al. (2015) proposed a method to build semantic parsing datasets by generating canonical utterances using a grammar and having crowdworkers paraphrase them into natural wording.

Semantic Parsing

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