VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning

Abstract: The ability to quickly learn from a small quantity oftraining data widens the range of machine learning applications. In this paper, we propose a data-efficient image captioning model, VisualGPT, which leverages the linguistic knowledge from a large pretrained LLM(LM). A crucial challenge is to balance between the use of visual information in the image and prior linguistic knowledge acquired from pretraining. We designed a novel self-resurrecting encoder-decoder attention mechanism to quickly adapt the pretrained LM as the language decoder ona small amount of in-domain training data. The proposed self-resurrecting activation unit produces sparse activations but has reduced susceptibility to zero gradients. We train the proposed model, VisualGPT, on 0.1%, 0.5% and 1% of MSCOCO and Conceptual Captions training data. Under these conditions, we outperform the best baseline model by up to 10.8% CIDEr on MS COCO and upto 5.4% CIDEr on Conceptual Captions. Further, Visual-GPT achieves the state-of-the-art result on IU X-ray, a medical report generation dataset. To the best of our knowledge, this is the first work that improves data efficiency of image captioning by utilizing LM pretrained on unimodal data. Our code is available at: https://github.com/Vision-CAIR/VisualGPT.

• The paper introduces a self-resurrecting encoder-decoder attention mechanism that preserves pretrained linguistic knowledge during image captioning.
• It demonstrates data-efficient PLM adaptation by boosting CIDEr scores up to 10% on MS COCO and 17.9% on Conceptual Captions using minimal in-domain data.
• The model's robust performance in low-data regimes paves the way for rapid deployment in applications like medical imaging and niche language domains.

A Critique and Overview of VisualGPT: Data-efficient Adaptation of Pretrained LLMs for Image Captioning

The paper "VisualGPT: Data-efficient Adaptation of Pretrained LLMs for Image Captioning" investigates a novel approach to enhance the data efficiency of image captioning by capitalizing on the linguistic capabilities of pretrained LLMs (PLMs) without necessitating extensive multimodal datasets. The scarcity of annotated data in specialized fields, such as medical imaging or low-resource languages, is a significant barrier to the application of state-of-the-art models in practical scenarios. VisualGPT offers a potential solution by optimizing PLM adaptation with minimal in-domain data.

The central innovation in VisualGPT is a novel self-resurrecting encoder-decoder attention mechanism. One of the salient features is the introduction of Self-Resurrecting Activation Units (SRAUs), which allow for sparse activations. This prevents unintentional overwriting of the linguistic knowledge captured during the pretraining of the LLMs. This mechanism uses gating operations to dynamically balance linguistic and visual information, ensuring the model can encode visual elements without losing the richness of the pretrained model's linguistic knowledge.

Key Numerical Results

Empirical studies show that VisualGPT achieves substantial improvements over baseline models, especially when trained on small datasets. Notably, when trained on as little as 0.1%, 0.5%, and 1% of MS COCO and Conceptual Captions datasets, VisualGPT delivers superior CIDEr scores compared to existing models. Specifically, VisualGPT surpasses baselines by up to 10.0% in the MS COCO dataset and 17.9% in Conceptual Captions, demonstrating notable effectiveness in low-data regimes. Additionally, on the IU X-ray dataset, VisualGPT sets a new benchmark in the domain of medical report generation.

Implications and Broader Impact

The proposed model represents a significant stride towards addressing the challenges of limited training data in resource-constrained environments. Adapting this approach, it can pave the way for more rapid prototyping and deployment of image captioning systems in fields where data is scarce or costly to obtain. Moreover, these advancements suggest potential pathways for leveraging PLMs beyond natural language processing and into multimodal tasks with minimal domain-specific adaptation.

The implications extend beyond immediate applications in image captioning to the broader development of algorithms capable of efficiently adapting to nuanced multimodal tasks. The methodology behind VisualGPT invites future research into progressively integrating the rich linguistic representations of PLMs with a wider array of visual tasks.

Future Directions

Looking into the future, several research directions can be identified. Firstly, expanding the adaptability of VisualGPT to other visual tasks beyond caption generation should be explored. The concept of using self-resurrecting gates could also be tested with different architecture paradigms and respective applications. Additionally, investigating the synergy between large-scale pretraining on diverse datasets and fine-tuning in low-resource scenarios will provide deeper insights into balancing efficiency and performance.

Finally, while the paper effectively demonstrates the utility of VisualGPT, examining the interpretability aspects and understanding the mechanism by which the self-resurrecting gates operate at a finer granularity would be a valuable pursuit. Assessing the model's robustness across different languages and cultural contexts will further validate its applicability in diverse settings.