Prismer: A Vision-Language Model with Multi-Task Experts (2303.02506v3)

Abstract: Recent vision-LLMs have shown impressive multi-modal generation capabilities. However, typically they require training huge models on massive datasets. As a more scalable alternative, we introduce Prismer, a data- and parameter-efficient vision-LLM that leverages an ensemble of task-specific experts. Prismer only requires training of a small number of components, with the majority of network weights inherited from multiple readily-available, pre-trained experts, and kept frozen during training. By leveraging experts from a wide range of domains, we show Prismer can efficiently pool this expert knowledge and adapt it to various vision-language reasoning tasks. In our experiments, we show that Prismer achieves fine-tuned and few-shot learning performance which is competitive with current state-of-the-arts, whilst requiring up to two orders of magnitude less training data. Code is available at https://github.com/NVlabs/prismer.

• The paper demonstrates that integrating pre-trained, task-specific experts enhances data and computational efficiency in vision-language models.
• The modular architecture, featuring an experts resampler and lightweight adaptor, enables robust performance on benchmarks like COCO Caption and VQAv2.
• The study highlights Prismer’s scalability and resilience, paving the way for future resource-efficient and adaptable AI systems.

Overview of Prismer: A Vision-LLM with Multi-Task Experts

Introduction

The research paper presents Prismer, a vision-LLM designed to efficiently leverage pre-trained, task-specific experts. Traditionally, vision-LLMs require extensive data and significant computational resources. Prismer offers a scalable alternative by integrating various pre-trained experts, minimizing the need for vast data sets and computational demands.

Architecture and Methodology

Prismer is built on an innovative architecture combining a vision encoder and an auto-regressive language decoder. Its primary strength lies in using pre-trained experts, which are divided into backbone experts and task experts. Backbone experts, such as CLIP's ViT and RoBERTa, handle general vision and language tasks, while task experts provide domain-specific insights.

Key components of Prismer include:

• Experts Resampler: This component efficiently converts variable-length expert input into fixed tokens, maintaining consistent memory usage.
• Lightweight Adaptor: Implemented within the transformer layers, it enhances the model's ability to handle multi-modal input.

The combination of these components ensures Prismer retains robust predictive capabilities while significantly reducing the requirement for training data.

Experimental Evaluation

Prismer's effectiveness was evaluated on standard benchmarks such as COCO Caption, NoCaps, and VQAv2. The model demonstrated competitive performance with significantly less pre-training data compared to contemporaries. For instance, Prismer achieved comparable results with state-of-the-art models trained on much larger datasets, exhibiting strong performance in both fine-tuned and zero-shot scenarios.

Implications and Future Directions

Prismer's architecture presents several key implications:

• Data Efficiency: By leveraging pre-trained experts, Prismer can perform competitively with significantly reduced data, illustrating a path toward more resource-efficient AI models.
• Scalability: The model's modular approach allows for easy integration of additional experts, making it adaptable to diverse tasks without extensive retraining.
• Robustness: Prismer shows resilience against noisy expert inputs, highlighting its potential in real-world applications where perfect data is rarely available.

The potential for future developments includes exploring different expert representations, enhancing adaptability to include new tasks with zero-shot proficiency, and further reducing training requirements while maintaining or improving performance metrics.

Conclusion

Prismer represents a significant stride in the development of vision-LLMs. By efficiently utilizing pre-trained experts, it offers a viable solution to the challenges of scale, data, and computational efficiency in AI. The model lays the groundwork for future research into modular and scalable AI systems that adapt dynamically to varied inputs and tasks.