Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics

TL;DR

This paper highlights the importance of context in image descriptions for accessibility, critiques current referenceless metrics for ignoring context, and proposes a context-aware version of CLIPScore to better evaluate descriptions for blind and low vision users.

Contribution

It identifies the limitations of existing referenceless metrics due to lack of context consideration and introduces a contextual CLIPScore to improve evaluation for accessibility.

Findings

Current metrics do not align with BLV users' needs.

Contextual information significantly impacts description quality.

Context-aware CLIPScore shows promise in better evaluation.

Abstract

Few images on the Web receive alt-text descriptions that would make them accessible to blind and low vision (BLV) users. Image-based NLG systems have progressed to the point where they can begin to address this persistent societal problem, but these systems will not be fully successful unless we evaluate them on metrics that guide their development correctly. Here, we argue against current referenceless metrics -- those that don't rely on human-generated ground-truth descriptions -- on the grounds that they do not align with the needs of BLV users. The fundamental shortcoming of these metrics is that they do not take context into account, whereas contextual information is highly valued by BLV users. To substantiate these claims, we present a study with BLV participants who rated descriptions along a variety of dimensions. An in-depth analysis reveals that the lack of context-awareness makes current referenceless metrics inadequate for advancing image accessibility. As a proof-of-concept, we provide a contextual version of the referenceless metric CLIPScore which begins to address the disconnect to the BLV data. An accessible HTML version of this paper is available at https://elisakreiss.github.io/contextual-description-evaluation/paper/reflessmetrics.html