multimodal-deep-learning

This paper introduces a dataset of 29,870 walkability ratings from 1,196 respondents and proposes a user-conditioned multimodal deep learning framework that fuses visual features with individual rater attributes to capture subjective variability in walkability perception. The model improves rank agreement by 65% over an image-only baseline, showing that who evaluates an environment matters.