Search Results for author:
Found 7 papers, 4 papers with code
Improving Drone Imagery For Computer Vision/Machine Learning in Wilderness Search and Rescue
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
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.
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings
Online texts -- across genres, registers, domains, and styles -- are riddled with human stereotypes, expressed in overt or subtle ways.
Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities
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.
Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis
Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities.
Language Informed Modeling of Code-Switched Text
Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities.
How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing
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.
