Florence-VL - A multimodal large language model jointly open-sourced by Microsoft and the University of Maryland.
Florence-VL is an innovative multimodal large-scale language model (MLLM) jointly developed by the University of Maryland and Microsoft Research. Florence-VL enriches visual representations with the generative visual foundation model Florence-2, enabling it to capture image...
What is Florence-VL?
Florence-VL is an innovative multimodal large-scale language model (MLLM) jointly developed by the University of Maryland and Microsoft Research. Florence-VL enriches visual representations with the generative visual foundation model Florence-2, capturing visual features at different levels and aspects of images, adapting to diverse downstream tasks. Florence-VL introduces Deep-Breadth Fusion (DBFusion) technology, achieving a deep fusion of visual and language understanding by combining visual features extracted from different depths and multiple cues.
The main functions of Florence-VL
- Multimodal understandingFlorence-VL can understand and process image and text data, achieving a deep fusion of vision and language.
- Visual feature extractionUsing the Florence-2 model, rich visual features are extracted from images.
- Deep-Breadth Fusion (DBFusion)It combines visual features of different levels (depth) and different task cues (breadth) to adapt to a variety of downstream tasks.
- Performance improvementAchieve performance improvements across multiple multimodal and visual center benchmarks, including VQA, OCR, and image captioning.
The technical principles of Florence-VL
- Generative visual encoderUsing Florence-2 as a visual encoder, visual features are generated based on different task cues, making it suitable for a variety of visual tasks.
- Feature fusion architectureA novel feature fusion architecture is introduced, which combines visual features extracted from Florence-2 with a pre-trained language model.
- Deep-Breadth Fusion (DBFusion):
- depthIt integrates visual features from different levels to capture conceptual details from low to high levels.
- Breadth: Using multiple task-specific visual features, each feature emphasizes different perceptual information in the input image.
- end-to-end pre-trainingThe entire model undergoes end-to-end pre-training to achieve optimal alignment between visual and language modalities.
- Fine-tuningAfter pre-training, the projection layer and language model are fine-tuned to adapt to specific downstream tasks.
Florence-VL project address
- Project official website:jiuhaichen.github.io/florence-vl
- GitHub repository:https://github.com/JiuhaiChen/Florence-VL
- arXiv technical paper:https://arxiv.org/pdf/2412.04424
Application scenarios of Florence-VL
- Researchers and scientistsScholars and researchers in the fields of artificial intelligence, computer vision, and natural language processing explore new algorithms, model architectures, and multimodal learning techniques.
- Software developersDevelopers enhance applications, for example, by improving the user experience through image recognition and processing capabilities.
- Data AnalystIn fields such as finance and market research, data analysts analyze and understand chart data to extract valuable information.
- educatorsTeachers and educational technology experts create interactive educational content to help students learn and understand complex concepts.
- Content creatorsWriters, journalists, and content creators generate image descriptions or provide inspiration for image content creation.