What could go wrong? Discovering and describing failure modes in computer vision

TL;DR

This paper introduces a method to predict and describe failure modes of computer vision models using natural language, enhancing interpretability and safety by identifying specific conditions causing model errors.

Contribution

It formalizes the Language-Based Error Explainability (LBEE) problem and proposes a joint vision-language embedding approach to describe model failures in natural language.

Findings

Method effectively identifies failure conditions in vision models.

Proposes metrics for evaluating language-based error explanations.

Demonstrates success in classification and segmentation tasks under challenging conditions.

Abstract

Deep learning models are effective, yet brittle. Even carefully trained, their behavior tends to be hard to predict when confronted with out-of-distribution samples. In this work, our goal is to propose a simple yet effective solution to predict and describe via natural language potential failure modes of computer vision models. Given a pretrained model and a set of samples, our aim is to find sentences that accurately describe the visual conditions in which the model underperforms. In order to study this important topic and foster future research on it, we formalize the problem of Language-Based Error Explainability (LBEE) and propose a set of metrics to evaluate and compare different methods for this task. We propose solutions that operate in a joint vision-and-language embedding space, and can characterize through language descriptions model failures caused, e.g., by objects unseen during training or adverse visual conditions. We experiment with different tasks, such as classification under the presence of dataset bias and semantic segmentation in unseen environments, and show that the proposed methodology isolates nontrivial sentences associated with specific error causes. We hope our work will help practitioners better understand the behavior of models, increasing their overall safety and interpretability.