Ambiguous Images With Human Judgments for Robust Visual Event Classification

TL;DR

This paper introduces SQUID-E, a dataset of ambiguous images with human uncertainty annotations, to evaluate and improve the robustness of visual event classification models on noisy, uncertain data.

Contribution

It presents a novel dataset of ambiguous images with human uncertainty labels and demonstrates its use in assessing and enhancing model performance on uncertain visual data.

Findings

Existing models struggle with ambiguous images.

The dataset reveals gaps in model calibration.

Ambiguous data can improve model robustness.

Abstract

Contemporary vision benchmarks predominantly consider tasks on which humans can achieve near-perfect performance. However, humans are frequently presented with visual data that they cannot classify with 100% certainty, and models trained on standard vision benchmarks achieve low performance when evaluated on this data. To address this issue, we introduce a procedure for creating datasets of ambiguous images and use it to produce SQUID-E ("Squidy"), a collection of noisy images extracted from videos. All images are annotated with ground truth values and a test set is annotated with human uncertainty judgments. We use this dataset to characterize human uncertainty in vision tasks and evaluate existing visual event classification models. Experimental results suggest that existing vision models are not sufficiently equipped to provide meaningful outputs for ambiguous images and that datasets of this nature can be used to assess and improve such models through model training and direct evaluation of model calibration. These findings motivate large-scale ambiguous dataset creation and further research focusing on noisy visual data.