Evaluating Text-to-Image Matching using Binary Image Selection (BISON)
| Autor: | Ishan Misra, Hexiang Hu, Laurens van der Maaten |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
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FOS: Computer and information sciences Computer Science - Computation and Language business.industry Computer science Computer Science - Artificial Intelligence Binary image Computer Vision and Pattern Recognition (cs.CV) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition computer.software_genre Task (project management) Image (mathematics) Visualization Artificial Intelligence (cs.AI) Selection (linguistics) Artificial intelligence business computer Image retrieval Computation and Language (cs.CL) Natural language processing |
| Zdroj: | ICCV Workshops |
| Popis: | Providing systems the ability to relate linguistic and visual content is one of the hallmarks of computer vision. Tasks such as text-based image retrieval and image captioning were designed to test this ability but come with evaluation measures that have a high variance or are difficult to interpret. We study an alternative task for systems that match text and images: given a text query, the system is asked to select the image that best matches the query from a pair of semantically similar images. The system's accuracy on this Binary Image SelectiON (BISON) task is interpretable, eliminates the reliability problems of retrieval evaluations, and focuses on the system's ability to understand fine-grained visual structure. We gather a BISON dataset that complements the COCO dataset and use it to evaluate modern text-based image retrieval and image captioning systems. Our results provide novel insights into the performance of these systems. The COCO-BISON dataset and corresponding evaluation code are publicly available from \url{http://hexianghu.com/bison/}. |
| Databáze: | OpenAIRE |
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