1. Multimodal Large Language Models (MLLMs) have shown success in various tasks, including language, vision, and vision-language activities under zero-shot and few-shot conditions. These models can perceive and generate answers using free-form texts based on generic modalities such as texts, pictures, and audio.
2. Grounding capability in multimodal big language models enhances their performance in vision-language tasks. It enables the model to interpret picture regions with geographical coordinates, allowing users to directly reference specific items or regions in the image instead of providing lengthy text descriptions.
3. Microsoft Research introduces KOSMOS-2, a multimodal big language model with grounding capabilities, built on KOSMOS-1. They train the model using the next-word prediction task based on Transformer and utilize a web-scale dataset of grounded image-text pairings. KOSMOS-2 performs well on grounding tasks, referring tasks, and language and vision-language tasks.
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ELI35 💁
- Language models that understand different types of information like text, images, and audio have become more versatile and can generate accurate responses.
- Microsoft Research developed a new model called KOSMOS-2, which understands pictures and can answer questions more precisely.
- The model performs well on various language and vision-related tasks and is available for testing on GitHub.
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