Performance Disparities of CLIP on Blind/Low Vision User Data

Core Concepts

CLIP's accuracy is significantly lower on images from blind/low vision users compared to web-crawled images due to sensitivities to image content, quality, and text content.

Abstract

The study evaluates CLIP's performance disparities on data from blind/low vision (BLV) users. It systematically assesses 25 CLIP variants in a zero-shot classification task, revealing a 15 percentage point accuracy gap between BLV and web-crawled images. The disparities stem from issues with image content recognition, quality robustness, and textual content understanding. Three common pre-training datasets are analyzed for disability content representation. Performance gaps extend to downstream models like OWL-ViT, CLIPSeg, and DALL-E2. Mitigation strategies include few-shot learning and application-level solutions.
Structure:

Introduction: Potential of AI for BLV assistance.
Data Disparities: Lower accuracy of CLIP on BLV data.
Image Content Sensitivity: Recognition challenges for disability objects.
Image Quality Impact: Effects of atypical framing, blur, viewpoint issues.
Textual Content Analysis: Recognition differences based on color vs material descriptions.
Downstream Model Impact: Performance disparities in OWL-ViT and DALL-E2.
Mitigation Strategies: Few-shot learning and application-level solutions.
Conclusion: Call for transparency in dataset reporting and equitable LMM development.

Stats

Testing 25 CLIP variants shows a 15% lower accuracy on BLV user images than web-crawled ones.
Disability objects have 25% lower recognition accuracy compared to non-disability objects with CLIP variants.
Large-scale datasets mention disability objects 16-17x less frequently than non-disability objects in captions.

Quotes

"We find that few-shot learning with as few as 5 images can mitigate CLIP’s quality-of-service disparities for BLV users."
"Disability objects are recognized less accurately by CLIP compared to non-disability objects."

Key Insights Distilled From

Explaining CLIP's performance disparities on data from blind/low vision users

by Dani... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2311.17315.pdf

Deeper Inquiries

How can the dataset composition be improved to address performance disparities?

To address performance disparities, improving dataset composition is crucial. One way to enhance dataset composition is by ensuring diversity and representation of marginalized communities, such as blind or low vision users. This can involve actively including images and descriptions of objects commonly used or encountered by these individuals in the training data. Additionally, datasets should incorporate a wide range of image qualities and variations that are typical in real-world scenarios faced by BLV users. By including more diverse and representative data, models like CLIP can better learn to recognize and classify objects specific to this user group.

What implications do these findings have for the broader use of LMMs in assistive technologies?

The findings from this study have significant implications for the broader use of Large Multi-Modal Models (LMMs) in assistive technologies, especially for individuals with visual impairments. It highlights the importance of evaluating LMMs on data from marginalized communities to ensure equitable performance across different user groups. The disparities identified underscore the need for tailored approaches when developing AI solutions for assistive technologies targeted at BLV users. By addressing these performance gaps through improved model training on diverse datasets, LMMs can be more effectively utilized in creating inclusive and accessible visual assistance tools.

How might the study's insights impact the design of future AI models for marginalized communities?

The insights gained from this study can significantly impact the design of future AI models intended for marginalized communities like blind or low vision users. Firstly, it emphasizes the necessity of incorporating diverse datasets that accurately represent the experiences and needs of these communities during model development. Future AI models should prioritize inclusivity by considering factors such as object recognition accuracy, image quality robustness, and sensitivity to descriptive language commonly used by BLV individuals.
Moreover, understanding how different aspects affect model performance allows researchers to tailor algorithms specifically towards addressing challenges faced by marginalized groups. By implementing strategies like few-shot learning techniques or personalized data augmentation methods based on individual user profiles within these communities, future AI models can deliver more accurate and reliable results in assisting visually impaired individuals with everyday tasks.