A Causal View of Compositional Zero-Shot Recognition
Abstract
People easily recognize new visual categories that are new combinations of known components. This compositional generalization capacity is critical for learning in real-world domains like vision and language because the long tail of new combinations dominates the distribution. Unfortunately, learning systems struggle with compositional generalization because they often build on features that are correlated with class labels even if they are not "essential" for the class. This leads to consistent misclassification of samples from a new distribution, like new combinations of known components.
Cite
Text
Atzmon et al. "A Causal View of Compositional Zero-Shot Recognition." Neural Information Processing Systems, 2020.Markdown
[Atzmon et al. "A Causal View of Compositional Zero-Shot Recognition." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/atzmon2020neurips-causal/)BibTeX
@inproceedings{atzmon2020neurips-causal,
title = {{A Causal View of Compositional Zero-Shot Recognition}},
author = {Atzmon, Yuval and Kreuk, Felix and Shalit, Uri and Chechik, Gal},
booktitle = {Neural Information Processing Systems},
year = {2020},
url = {https://mlanthology.org/neurips/2020/atzmon2020neurips-causal/}
}