Ambiguous Images With Human Judgments for Robust Visual Event Classification

Kate Sanders, Reno Kriz, Anqi Liu, Benjamin Van Durme

Keywords: Datasets, Computer Vision, Cognitive Science, Uncertainty, Perception, Model Calibration

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.

Author Statement: Yes

TL;DR: We introduce a procedure for creating datasets of ambiguous images and use it to produce DAI (Dataset of Ambiguous Images), a collection of noisy images extracted from videos and corresponding human uncertainty judgments.

Dataset Url: https://github.com/katesanders9/squid-e

License:

CC-BY 4.0
https://creativecommons.org/licenses/by/4.0/

Contribution Process Agreement: Yes

In Person Attendance: Yes

URL: https://katesanders9.github.io/ambiguous-images/

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/ambiguous-images-with-human-judgments-for/code)