ISU-Test combines rendering-based scene generation with search-based testing to produce up to 10x higher failure rates and 3.6x higher failure coverage in VLMs for in-car scene understanding compared to random generation.
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
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abstract
Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem, viz., models that cast description as either generation problem or as a retrieval problem over a visual or multimodal representational space. We provide a detailed review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image datasets and the evaluation measures that have been developed to assess the quality of machine-generated image descriptions. Finally we extrapolate future directions in the area of automatic image description generation.
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cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Search-based Testing of Vision Language Models for In-Car Scene Understanding
ISU-Test combines rendering-based scene generation with search-based testing to produce up to 10x higher failure rates and 3.6x higher failure coverage in VLMs for in-car scene understanding compared to random generation.