What is InternVL
InternVL is a multimodal large model launched by OpenGVLab of Shanghai AI Laboratory, focusing on vision and language tasks. It adopts a ViT-MLP-LLM architecture and, through the integration of a vision module (such as InternViT) and a language module (such as InternLM), achieves a deep combination of vision and language. InternVL is trained on massive web-scale image-text data and supports multiple modality inputs, such as images, videos, and text, and can generate multilingual outputs.
Main features of InternVL
- Multimodal understanding: Can process and understand information from different modalities (such as text, images, videos, etc.).
- Multidisciplinary reasoning: Performs complex reasoning and problem solving across multiple subject areas.
- Multilingual processing: Supports understanding and generation in multiple languages.
- Pure language processing: Performs language tasks such as text analysis, generation, and understanding.
- Document and chart understanding: Can effectively recognize and interpret text in document images and supports zero-shot learning tasks.
- Infographic question answering: Performs excellently in infographic question answering tasks.
- Scene text understanding: Can understand and process text information in scenes.
- Science and math problem solving: Has strong capabilities in science and math problem solving.
- Multimodal hallucination detection: Identifies and distinguishes real and fictitious visual information.
- Visual grounding: Matches textual descriptions with actual objects in images.
Technical principles of InternVL
- Vision Encoder: Uses an improved Vision Transformer (ViT) model, such as InternViT. It is responsible for converting input images or videos into high-dimensional feature vectors and extracting visual information.
- MLP Projector: Used to map visual features into the same feature space as the language model so that the two can be effectively integrated.
- Language Model (LLM): As the base model, it is responsible for processing text input and generating text output, based on InternLM.
- Dynamic High Resolution: By splitting images into multiple small blocks (tiles) and dynamically adjusting the resolution, the model can efficiently process high-resolution images while maintaining computational efficiency.
- Pixel Shuffle: By reducing the number of visual tokens, it lowers computational complexity while preserving detailed image information.
- Progressive Training Strategy: First uses a small model for pretraining on large amounts of noisy data, then uses a large model for alignment on curated data, thereby reducing training resource consumption.
- Multimodal input and output: Supports multiple input modalities such as text, images, and videos, and can generate multiple output formats such as images, bounding boxes, and masks.
- Pretraining stage: Trains the vision encoder (such as InternViT) and the MLP projector while freezing the weights of the language model.
- Fine-tuning stage: Unfreezes all parameters of the vision encoder, MLP projector, and language model for joint training.
InternVL project address
- Github repository：https://github.com/OpenGVLab/InternVL
- arXiv technical paper：https://arxiv.org/pdf/2312.14238
- Online Demo：https://huggingface.co/spaces/OpenGVLab/InternVL
Application Scenarios of InternVL
- Visual Question Answering (VQA): InternVL can handle questions related to image or video content and is widely used in fields such as education, e-commerce, and customer service.
- Document and Chart Understanding: InternVL performs excellently in document understanding (DocVQA) and infographic question answering (ChartQA) tasks. It can extract key information from documents, parse tables and charts, and generate document summaries or chart explanations.
- Multilingual Translation and Understanding: InternVL supports multilingual processing and can process and generate text in multiple languages. It has broad application prospects in cross-language communication and international business, helping users quickly translate and understand documents in different languages.
- Image and Video Analysis: InternVL can be used for automatic annotation, classification, and understanding of image and video content. In the field of security monitoring, it can analyze surveillance videos in real time and identify abnormal behavior; in content moderation, it can quickly identify violating content.
- Intelligent Customer Service: InternVL can serve as the core technology of intelligent customer service, supporting multimodal interaction. Users can describe problems by uploading images or videos, and the model can understand them and provide solutions.