WearVQA: A Visual Question Answering Benchmark for Wearables in Egocentric Authentic Real-world scenarios
This addresses the need for robust AI assistants on wearables by providing a challenging benchmark, though it is incremental as it builds on existing VQA benchmarks with a domain-specific focus.
The authors tackled the problem of evaluating Visual Question Answering (VQA) for wearable devices by introducing WearVQA, a benchmark with 2,520 image-question-answer triplets that reflect real-world challenges like poor image quality, resulting in low QA accuracy of 24-52% for multi-model AI systems.
We introduce WearVQA, the first benchmark specifically designed to evaluate the Visual Question Answering (VQA) capabilities of multi-model AI assistant on wearable devices like smart glasses. Unlike prior benchmarks that focus on high-quality, third-person imagery, WearVQA reflects the unique challenges of ego-centric interaction-where visual inputs may be occluded, poorly lit, unzoomed, or blurry, and questions are grounded in realistic wearable use cases. The benchmark comprises 2,520 carefully curated image-question-answer triplets, spanning 7 diverse image domains including both text-centric and general scenes, 10 cognitive task types ranging from basic recognition to various forms of reasoning, and 6 common wearables-specific image quality issues. All questions are designed to be answerable using only the visual input and common senses. WearVQA is paired with a rigorous LLM-as-a-judge evaluation framework with 96% labeling accuracy. Open-source and proprietary multi-model LLMs achieved a QA accuracy as low as 24-52% on WearVQA, with substantial drops on lower-quality images and reasoning-heavy tasks. These observations position WearVQA as a comprehensive and challenging benchmark for guiding technical advancement towards robust, real-world multi-model wearables AI systems.