New Flamingo Visual Language Model Excels at Few-Shot Learning Tasks
DeepMind introduces Flamingo, a visual language model that seamlessly processes interleaved images, videos, and text to master new tasks with only a handful of examples. The system outperforms heavily fine-tuned models across a wide range of visual benchmarks.
Researchers introduce Flamingo, a family of Visual Language Models (VLMs) designed to tackle novel tasks using only a handful of annotated examples. The architecture bridges pretrained vision-only and language-only models to process sequences of arbitrarily interleaved visual and textual data. This innovative design allows the system to seamlessly ingest and understand both images and videos as inputs.
The model trains on large-scale multimodal web corpora that contain mixed text and images, which provides Flamingo with powerful in-context few-shot learning capabilities. By simply prompting the model with a few task-specific examples, it rapidly adapts to new challenges without needing extensive task-specific fine-tuning. This approach makes the system highly flexible and efficient for real-world applications.
Evaluations show that a single Flamingo model achieves state-of-the-art results across a broad spectrum of image and video tasks. It handles open-ended tasks like visual question-answering and scene captioning, as well as close-ended tasks like multiple-choice questions. Impressively, Flamingo outperforms specialized models that require thousands of times more task-specific training data to achieve comparable results.