Beyond Readability: Evaluating Task Information Recoverability
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Abstract (arXiv)
Direct visual readability and task-information recoverability are different quantities. Failure to decode a target from a fixed observation need not eliminate access to that target through another recovery route. We develop an evaluation perspective that makes the observation, query, target, and available knowledge explicit and measures the overlap between routes' success sets. For information available on the original visible surface under suitable imaging conditions, direct optical recovery reads the target from the image, optionally after restoration; entity-linked recovery uses residual visual evidence to identify the depicted entity and accesses its target through an entity--attribute relation in a specified knowledge resource. Such access can draw on stored knowledge or an external source. A controlled book-cover study instantiates external access with a fixed title--author catalog, comparing optical author recovery with visual title resolution and deterministic lookup under resolution degradation. Entity-linked successes persist across the tested vision--language models, revealing information access beyond the tested direct visual frontier despite substantial differences in absolute performance. A substantial optical-only region remains. These complementary outcomes show why visual degradation should be evaluated through the task information accessible along specified routes and knowledge resources, alongside direct readability.
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References · 14
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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- 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
- 2019OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledgecited by 1,121
- 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 970
- 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledgecited by 710
- 2022Activating More Pixels in Image Super-Resolution Transformercited by 438
- 2023Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?cited by 207
- 2023Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entitiescited by 113
- 2023Encyclopedic VQA: Visual questions about detailed properties of fine-grained categoriescited by 107
- 2020Scene Text Image Super-Resolution in the Wildcited by 63
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