# Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challenges

arXiv: https://arxiv.org/abs/2408.03303 · HTML: https://mlsanity.com/paper/2408.03303/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 94 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 18 of 18)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects](https://mlsanity.com/paper/1811.11553/) (2018)
- [Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis](https://mlsanity.com/paper/2202.10553/) (2022)
- [Assessing Image Quality Issues for Real-World Problems](https://mlsanity.com/paper/2003.12511/) (2020)
- [Investigating Use Cases of AI-Powered Scene Description Applications for Blind and Low Vision People](https://mlsanity.com/paper/2403.15604/) (2024)
- [A Study on the Challenges of Using Robotics Simulators for Testing](https://mlsanity.com/paper/2004.07368/) (2020)
- [Crowdsourcing the Perception of Machine Teaching](https://mlsanity.com/paper/2002.01618/) (2020)
- [Explaining CLIP's performance disparities on data from blind/low vision users](https://mlsanity.com/paper/2311.17315/) (2023)
- [Blind Users Accessing Their Training Images in Teachable Object Recognizers](https://mlsanity.com/paper/2208.07968/) (2022)
- [What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?](https://mlsanity.com/paper/2210.14966/) (2022)
- [Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective](https://mlsanity.com/paper/2003.12122/) (2020)
- [From the Lab to People's Home: Lessons from Accessing Blind Participants' Interactions via Smart Glasses in Remote Studies](https://mlsanity.com/paper/2203.04282/) (2022)
- [Helping Visually Impaired People Take Better Quality Pictures](https://mlsanity.com/paper/2305.08066/) (2023)

## Built on by (most cited first, 5 of 5)

- [Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions](https://mlsanity.com/paper/2507.15692/) (2025)
- ["It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models](https://mlsanity.com/paper/2511.08917/) (2025)
- [How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People](https://mlsanity.com/paper/2602.13469/) (2026)
- [Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind Users](https://mlsanity.com/paper/2602.16930/) (2026)
- [Lost in Instructions: Study of Blind Users' Experiences with DIY Manuals and AI-Rewritten Instructions for Assembly, Operation, and Troubleshooting of Tangible Products](https://mlsanity.com/paper/2602.18630/) (2026)

## Abstract (arXiv)

Object recognition technologies hold the potential to support blind and low-vision people in navigating the world around them. However, the gap between benchmark performances and practical usability remains a significant challenge. This paper presents a study aimed at understanding blind users' interaction with object recognition systems for identifying and avoiding errors. Leveraging a pre-existing object recognition system, URCam, fine-tuned for our experiment, we conducted a user study involving 12 blind and low-vision participants. Through in-depth interviews and hands-on error identification tasks, we gained insights into users' experiences, challenges, and strategies for identifying errors in camera-based assistive technologies and object recognition systems. During interviews, many participants preferred independent error review, while expressing apprehension toward misrecognitions. In the error identification task, participants varied viewpoints, backgrounds, and object sizes in their images to avoid and overcome errors. Even after repeating the task, participants identified only half of the errors, and the proportion of errors identified did not significantly differ from their first attempts. Based on these insights, we offer implications for designing accessible interfaces tailored to the needs of blind and low-vision users in identifying object recognition errors.