Cross Modal Benchmark

Cross-modal benchmarks evaluate the performance of models that process and integrate information from different modalities, such as text and images. Current research focuses on developing and applying large-scale multimodal models, often based on transformer architectures, to improve performance on tasks like image captioning, visual question answering, and anomaly detection across diverse domains. These benchmarks are crucial for advancing the field of multimodal learning, driving improvements in model architectures and algorithms, and enabling the development of more robust and versatile AI systems for various applications. The creation of new, large-scale, and diverse benchmarks, particularly in under-represented languages, is a significant area of ongoing development.