Captioning Benchmark

Image and video captioning benchmarks are crucial for evaluating the ability of vision-language models to generate accurate and detailed textual descriptions of visual content. Current research focuses on developing more comprehensive benchmarks with longer, more structured captions, improving evaluation metrics to better align with human judgment, and exploring novel model architectures, such as transformer-based models and those incorporating streaming or memory mechanisms, to handle longer videos and generate richer descriptions. These advancements are vital for improving the performance and reliability of multimodal AI systems, with applications ranging from automated content description to assistive technologies.