173 citations · 179 across the 4 of their papers we have counts for
4 papers
Thinking Like an Annotator: Generation of Dataset Labeling Instructions
Nadine Chang, Francesco Ferroni, Michael J. Tarr +2
Large-scale datasets are essential to modern day deep learning. Advocates argue that understanding these methods requires dataset transparency (e.g. "dataset curation, motivation,…
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
Nadine Chang, Zhiding Yu, Yu-Xiong Wang +3
Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resamp…
AlphaNet: Improving Long-Tail Classification By Combining Classifiers
Nadine Chang, Jayanth Koushik, Aarti Singh +3
Methods in long-tail learning focus on improving performance for data-poor (rare) classes; however, performance for such classes remains much lower than performance for more data-r…
BOLD5000: A public fMRI dataset of 5000 images
Nadine Chang, John A. Pyles, Abhinav Gupta +2
Vision science, particularly machine vision, has been revolutionized by introducing large-scale image datasets and statistical learning approaches. Yet, human neuroimaging studies…