Visual Topics via Visual Vocabularies
Abstract
Researchers have long used topic modeling to automatically characterize and summarize text documents without supervision. Can we extract similar structures from collections of images? To do this, we propose visual vocabularies, a method to analyze image datasets by decomposing images into segments, and grouping similar segments into visual "words". These vocabularies of visual "words" enable us to extract visual topics that capture hidden themes distinct from what is captured by classic unsupervised approaches. We evaluate our visual topics using standard topic modeling metrics and confirm the coherency of our visual topics via a human study.
Cite
Text
Havaldar et al. "Visual Topics via Visual Vocabularies." NeurIPS 2023 Workshops: XAIA, 2023.Markdown
[Havaldar et al. "Visual Topics via Visual Vocabularies." NeurIPS 2023 Workshops: XAIA, 2023.](https://mlanthology.org/neuripsw/2023/havaldar2023neuripsw-visual/)BibTeX
@inproceedings{havaldar2023neuripsw-visual,
title = {{Visual Topics via Visual Vocabularies}},
author = {Havaldar, Shreya and You, Weiqiu and Ungar, Lyle and Wong, Eric},
booktitle = {NeurIPS 2023 Workshops: XAIA},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/havaldar2023neuripsw-visual/}
}