Flamingo: a Visual Language Model for Few-Shot Learning (2204.14198v2)

Abstract: 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 LLMs (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.

Flamingo: A Visual LLM for Few-Shot Learning

In this paper, the authors introduce Flamingo, a novel Visual LLM (VLM) addressing critical challenges in multimodal machine learning, specifically few-shot learning. The primary objectives of Flamingo are to bridge powerful pretrained vision-only and language-only models, handle arbitrary interleaving of visual and textual data, and ingest images or videos seamlessly as inputs. This overview synthesizes the paper's core methodologies, evaluation, and contributions to advancing AI research.

Flamingo combines state-of-the-art pretrained models in vision (e.g., a contrastively pretrained NFNet-F6) and text (e.g., Chinchilla 70B) with new architectural innovations to facilitate effective multimodal interactions. The architecture notably includes a Perceiver Resampler, designed to condense large spatio-temporal grids from visual inputs into a fixed number of tokens, and gated cross-attention dense layers that integrate visual information into a frozen text transformer.

Core Methodologies

1. Perceiver Resampler: This module facilitates the processing of arbitrary-sized visual inputs by condensing spatio-temporal features from images or videos into a fixed set of tokens. The Perceiver Resampler leverages learned latent queries and a cross-attention mechanism to effectively bridge the vision encoder and the text transformer.
2. Gated Cross-Attention Dense Layers: Interleaved between layers of a frozen pretrained LLM, these layers are specifically designed to introduce visual features while maintaining the integrity of the LLM’s pretrained knowledge. This is achieved using a $\tanh$-gating mechanism that ensures the new layers do not disrupt the pretrained model’s outputs at initialization.
3. Few-Shot Learning with Multi-Visual Input Support: The ability to manage interleaved and arbitrary sequences of text and visuals makes Flamingo suitable for few-shot learning. The model predicts texts conditioned on visual inputs, with attention mechanisms selectively focusing on the most relevant contextual images or videos.

Evaluation and Performance

The Flamingo models were evaluated extensively across 16 multimodal benchmarks covering diverse tasks such as visual question-answering, captioning, visual dialogue, and image/video classification. The evaluation spans both zero-shot and few-shot learning scenarios, underscoring Flamingo's versatile adaptability. Key findings include:

• Few-Shot Learning: Flamingo sets a new state-of-the-art in few-shot learning for several benchmarks. Notably, it surpasses fine-tuned models on six out of 16 benchmarks using only 32 task-specific examples, demonstrating significant efficiency in data usage.
• Scaling: Larger Flamingo models show improved performance with increased model size and number of shots, aligning with scaling trends observed in LLMs like GPT-3.
• Fine-Tuning Capability: While few-shot learning is highlighted, fine-tuning remains robust for scenarios with substantial annotated data, with Flamingo setting new state-of-the-art on additional challenging benchmarks upon fine-tuning.

Ablation Studies and Insights

Comprehensive ablation studies underscore the importance of:

• Training on diverse datasets, with different combinations of image-text pairs and interleaved data boosting performance.
• Freezing the pretrained LLM to prevent catastrophic forgetting.
• Effective dataset accumulation strategies over simpler round-robin updates.

Implications and Future Directions

Practical Implications: Flamingo's capability to handle few-shot learning with minimal task-specific data lowers the barrier for applying advanced VLMs in various practical scenarios, including low-resource settings and interactive applications like visual dialogue systems. This flexibility is particularly beneficial in domains where large annotated datasets are not readily available.

Theoretical Implications: The success of integrating vision and text through components like the Perceiver Resampler and gated cross-attention layers opens avenues for further research into more efficient and generalizable VLM architectures. Future research could explore unified models that simultaneously achieve high performance on both classification and generative tasks.

Speculative Future Developments: The potential for enhancing the current model includes exploring the combination of few-shot learning with gradient-based fine-tuning approaches to leverage larger numbers of few-shot examples effectively. Additionally, improving model performance on structured output tasks and incorporating additional modalities like audio could significantly broaden Flamingo's applicability.

Conclusion

Flamingo represents a significant advancement in the field of multimodal machine learning, particularly in its application to few-shot learning scenarios. By leveraging powerful pretrained vision and LLMs while introducing novel architectural components, Flamingo demonstrates impressive adaptability and efficiency across a wide range of tasks. Its contributions are poised to influence future developments in general-purpose visual understanding, promoting broader accessibility and application of advanced AI technologies.