Flamingo: a Visual Language Model for Few-Shot Learning
Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propose key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We perform a thorough evaluation of our models, exploring and measuring their ability to rapidly adapt to a variety of image and video tasks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer; captioning tasks, which evaluate the ability to describe a scene or an event; and close-ended tasks such as multiple-choice visual question-answering. For tasks lying anywhere on this spectrum, a single Flamingo model can achieve a new state of the art with few-shot learning, simply by prompting the model with task-specific examples. On numerous benchmarks, Flamingo outperforms models fine-tuned on thousands of times more task-specific data.
PDF Abstract DeepMind 2022 PDFCode
Spaces
Tasks
Few-Shot Learning
Generative Visual Question Answering
Language Modelling
Medical Visual Question Answering
Meme Classification
Multiple-choice
Question Answering
Temporal/Casual QA
Video Question Answering
Video Understanding
Visual Question Answering
Visual Question Answering (VQA)
Zero-Shot Cross-Modal Retrieval
Zero-Shot Learning
Zero-Shot Video Question Answer
MS COCO
Flickr30k
Visual Question Answering v2.0
OK-VQA
TextVQA
VizWiz
YouCook2
Hateful Memes
VisDial
NExT-QA
VATEX
iVQA
STAR Benchmark
RareAct
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| Zero-Shot Cross-Modal Retrieval | COCO 2014 | Flamingo | Image-to-text R@1 | 65.9 | # 9 | ||
| Image-to-text R@5 | 87.3 | # 8 | |||||
| Image-to-text R@10 | 92.9 | # 7 | |||||
| Text-to-image R@1 | 48.0 | # 9 | |||||
| Text-to-image R@5 | 73.3 | # 9 | |||||
| Text-to-image R@10 | 82.1 | # 8 | |||||
| Zero-Shot Cross-Modal Retrieval | Flickr30k | Flamingo | Image-to-text R@1 | 89.3 | # 10 | ||
| Image-to-text R@5 | 98.8 | # 11 | |||||
| Image-to-text R@10 | 99.7 | # 7 | |||||
| Text-to-image R@1 | 79.5 | # 8 | |||||
| Text-to-image R@5 | 95.3 | # 7 | |||||
| Text-to-image R@10 | 97.9 | # 5 | |||||
| Meme Classification | Hateful Memes | Flamingo (few-shot:32) | ROC-AUC | 0.700 | # 12 | ||
| Meme Classification | Hateful Memes | Flamingo (fine-tuned) | ROC-AUC | 0.866 | # 3 | ||
| Visual Question Answering (VQA) | MSRVTT-QA | Flamingo (32-shot) | Accuracy | 0.310 | # 32 | ||
| Visual Question Answering (VQA) | MSRVTT-QA | Flamingo (0-shot) | Accuracy | 0.174 | # 34 | ||
| Visual Question Answering (VQA) | MSRVTT-QA | Flamingo | Accuracy | 0.474 | # 5 | ||
| Temporal/Casual QA | NExT-QA | Flamingo(32-shot) | WUPS | 33.5 | # 4 | ||
| Temporal/Casual QA | NExT-QA | Flamingo(0-shot) | WUPS | 26.7 | # 7 | ||
| Visual Question Answering (VQA) | OK-VQA | Flamingo80B | Accuracy | 50.6 | # 18 | ||
| Visual Question Answering (VQA) | OK-VQA | Flamingo9B | Accuracy | 44.7 | # 23 | ||
| Visual Question Answering (VQA) | OK-VQA | Flamingo3B | Accuracy | 41.2 | # 26 | ||
| Generative Visual Question Answering | PMC-VQA | Open-Flamingo | BLEU-1 | 4.1 | # 3 | ||
| Medical Visual Question Answering | PMC-VQA | Open-Flamingo | Accuracy | 26.4 | # 2 | ||
| Visual Question Answering (VQA) | PMC-VQA | Open-Flamingo | Accuracy | 26.4 | # 2 | ||
| Action Recognition | RareAct | 🦩 Flamingo | mWAP | 60.8 | # 1 | ||
| Video Question Answering | STAR Benchmark | Flamingo-9B (0-shot) | Average Accuracy | 41.8 | # 15 | ||
| Video Question Answering | STAR Benchmark | Flamingo-9B (4-shot) | Average Accuracy | 42.8 | # 13 | ||
| Video Question Answering | STAR Benchmark | Flamingo-80B (0-shot) | Average Accuracy | 39.7 | # 16 | ||
| Video Question Answering | STAR Benchmark | Flamingo-80B (4-shot) | Average Accuracy | 42.4 | # 14 | ||
| Zero-Shot Video Question Answer | STAR Benchmark | Flamingo-9B | Accuracy | 41.8 | # 2 | ||
| Visual Question Answering (VQA) | VQA v2 test-dev | Flamingo 80B | Accuracy | 56.3 | # 51 | ||
| Visual Question Answering (VQA) | VQA v2 test-dev | Flamingo 9B | Accuracy | 51.8 | # 54 | ||
| Visual Question Answering (VQA) | VQA v2 test-dev | Flamingo 3B | Accuracy | 49.2 | # 57 |
