MANTIS revolutionizes how multimodal models handle multi-image inputs. By marrying instruction tuning with multi-image datasets, it showcases a significant leap in performance across both multi-image and single-image visual language tasks.

Remarkable Achievements:

• Utilizes Mantis-Instruct, a specially designed dataset that enriches model training.
• Demonstrates state-of-the-art results in multi-image benchmarks, outperforming other LMMs.
• Provides a more cost-effective and resource-efficient approach compared to extensive pre-training.

This work is a step forward in maximizing the potential of visual language models in understanding and interacting with complex visual situations. Further research could explore the integration of these models with real-time video processing for tasks like surveillance or environment monitoring.