The seamless integration of vision and language has been a focus of recent advancements in AI. The sector has seen significant progress with the appearance of LLMs. Yet, developing vision and vision-language foundation models essential for multimodal AGI systems still have to catch up. This gap has led to the creation of a groundbreaking model proposed by researchers from Nanjing University, OpenGVLab, Shanghai AI Laboratory, The University of HongKong, The Chinese University of Hong Kong, Tsinghua University, University of Science and Technology of China, SenseTime Research referred to as InternVL, which scales up vision foundation models and aligns them for generic visual-linguistic tasks.

InternVL addresses a critical issue within the realm of artificial intelligence: the disparity in the event pace between vision foundation models and LLMs. Existing models often use basic glue layers to align vision and language features, leading to a mismatch in parameter scales and representation consistency. This inadequacy can hinder the complete potential of LLMs. 

The methodology behind InternVL is each unique and robust. The model employs a large-scale vision encoder, InternViT-6B, and a language middleware, QLLaMA, with 8 billion parameters. This structure serves a dual purpose: it functions as an independent vision encoder for perception tasks. It collaborates with the language middleware for complex vision-language tasks and multimodal dialogue systems. The model’s training involves a progressive alignment strategy, starting with contrastive learning on extensive noisy image-text data after which moving to generative learning with more refined data. This progressive approach consistently improves the model’s performance across various tasks.

InternVL demonstrates its prowess by outperforming existing methods in 32 generic visual-linguistic benchmarks, a testament to its robust visual capabilities. The model excels in diverse tasks comparable to image and video classification, image and video-text retrieval, image captioning, visible query answering, and multimodal dialogue. This diverse range of capabilities is attributed to the aligned feature space with LLMs, enabling the model to handle complex tasks with remarkable efficiency and accuracy.

Key facets of InternVL’s performance include:

• The model is flexible as a standalone vision encoder or combined with the language middleware for various tasks.
• InternVL innovatively overcomes this by scaling the vision foundation model to a remarkable 6 billion parameters, facilitating a more comprehensive and effective integration with LLMs.
• Its ability to realize state-of-the-art performance across 32 generic visual-linguistic benchmarks highlights its advanced visual capabilities.
• Effective performance in image and video classification, image and video-text retrieval, image captioning, visual query answering, and multimodal dialogue.
• The aligned feature space with LLMs enhances its capability to seamlessly integrate with existing language models, further broadening its application scope.

In conclusion, the research conducted may be presented in a nutshell in the next points:

• InternVL represents a serious leap in multimodal AGI systems, bridging a vital gap in developing vision and vision-language foundation models.
• Its progressive scaling and alignment strategy endow it with versatility and power, enabling superior performance across various visual-linguistic tasks.
• This research contributes to advancing multimodal large models, potentially reshaping the longer term landscape of AI and machine learning.

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