Image Captioners Are Scalable Vision Learners Too

Abstract: Contrastive pretraining on image-text pairs from the web is one of the most popular large-scale pretraining strategies for vision backbones, especially in the context of large multimodal models. At the same time, image captioning on this type of data is commonly considered an inferior pretraining strategy. In this paper, we perform a fair comparison of these two pretraining strategies, carefully matching training data, compute, and model capacity. Using a standard encoder-decoder transformer, we find that captioning alone is surprisingly effective: on classification tasks, captioning produces vision encoders competitive with contrastively pretrained encoders, while surpassing them on vision & language tasks. We further analyze the effect of the model architecture and scale, as well as the pretraining data on the representation quality, and find that captioning exhibits the same or better scaling behavior along these axes. Overall our results show that plain image captioning is a more powerful pretraining strategy than was previously believed.

• The paper demonstrates that image captioning pretraining can rival and sometimes exceed contrastive methods in fine-grained vision-language tasks.
• It introduces the CapPa procedure, alternating autoregressive and parallel predictions, which boosts classification accuracy and few-shot performance.
• The study shows that captioning models scale effectively with data and integrate well with language models for enhanced multi-modal applications.

Analysis of "Image Captioners Are Scalable Vision Learners Too"

The research paper "Image Captioners Are Scalable Vision Learners Too" presents an in-depth investigation comparing contrastive pretraining and image captioning for training vision encoders from image-text data. The research challenges the prevailing perception that contrastive models are superior to captioning approaches and demonstrates underappreciated merits of image captioning models.

Key Findings and Contributions

1. Comparison of Pretraining Strategies: The researchers rigorously compare contrastive and captioning pretraining strategies using vision encoders. They find that image captioning, typically deemed less effective, actually yields competitive and sometimes superior results. Especially notable is that vision encoders, pretrained with image captioning, perform well in vision-language tasks and fine-grained classification scenarios. This indicates potential biases in prior evaluations focusing chiefly on zero-shot classification.
2. CapPa Pretraining Procedure: A novel alternation between autoregressive and parallel prediction—termed CapPa—yields surprising enhancements in the scalability and efficacy of pretraining through image captioning. CapPa demonstrates significant gains in classification accuracy and performs well in few-shot classification scenarios, thus underscoring its potential for large-scale applications.
3. Scaling Properties: The paper reveals that the captioning approach displays favorable scaling properties in terms of data and model size, suggesting potential for better results at larger scales.
4. Integration with LLMs: The authors explore integrating the generated vision encoders with pretrained LLMs. They show that captioning-pretrained encoders synergize well with these LLMs, supporting applications like image captioning and visual question answering (VQA).
5. Evaluation on Benchmark Tasks: In rigorous benchmarks such as ARO and SugarCrepe, which assess sensitivity to relational and ordering mutations in captions, CapPa models significantly outperform contrastive models. This highlights their enhanced interpretative abilities on detailed and structured image captions, signaling their potential for multi-modal applications.

Implications and Future Directions

The insights from this paper suggest a revisitation of current pretraining strategies within the domain of vision-LLMs. The demonstrated benefits of captioning approaches should encourage further research and development. Specifically:

• Robust Performance in Multi-Modal and Fine-Grained Settings: The use of captioners should be considered in domains requiring an understanding of fine semantic details, such as autonomous systems and medical imaging, where fine-grained distinctions are vital.
• Efficiency in Model Utilization: The flexibility of CapPa models to efficiently integrate with existing LLMs suggests opportunities for leveraging pre-existing resources in developing new AI systems without retraining from scratch.
• Enhancements on Large-Scale Applications: Given the favorable scaling properties observed, deploying CapPa models at a larger infrastructure scale could unlock further improvements, aligning with increased data availability.
• Computational Trade-offs: The efficiency in architecture choice and training strategy may stimulate discussion regarding computational resource allocation and strategy selection, particularly in large AI systems.

In conclusion, this research signifies a reevaluation of traditional biases favoring contrastive pretraining, providing evidence that image captioning can be an equally viable, if not superior, pretraining approach for vision encoders in multi-modal AI applications. Future investigations could pivot towards optimizing captioning architectures and exploring their symbiotic potential with LLMs to enhance AI's interpretative capabilities in complex environments.