Reproducibility Study of "Languange-Image COnsistency"

TMLR Paper2247 Authors

Abstract:

This report aims to verify the findings and expand upon the evaluation and training methods from the paper LICO: Explainable Models with Language-Image COnsistency. The
main claims are that LICO (i) enhances interpretability by producing more explainable
saliency maps in conjunction with a post-hoc explainability method and (ii) improves image classification performance without computational overhead during inference. We have
reproduced the key experiments conducted by Lei et al.; however, the obtained results
do not support the original claims. Additionally, we identify a vulnerability in the paper’s main evaluation method that favors non-robust models, and propose robust experimental setups for quantitative analysis. Furthermore, we undertake additional studies
on LICO’s training methodology to enhance its interpretability. Our code is available at
https://anonymous.4open.science/r/lico-reproduction-7FEB.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Krzysztof_Jerzy_Geras1

Submission Number: 2247