Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning
This work addresses inefficiencies in visual language models for image captioning, offering a more parameter-efficient and adaptable solution, though it is incremental as it builds upon existing Flamingo models.
The paper tackles the problem of large parameter requirements and inefficient data incorporation in visual language models for image captioning by introducing Re-ViLM, a retrieval-augmented model that uses an external database, achieving significant performance boosts in zero-shot and few-shot out-of-domain settings with 4 times fewer parameters compared to baselines.
Augmenting pretrained language models (LMs) with a vision encoder (e.g., Flamingo) has obtained the state-of-the-art results in image-to-text generation. However, these models store all the knowledge within their parameters, thus often requiring enormous model parameters to model the abundant visual concepts and very rich textual descriptions. Additionally, they are inefficient in incorporating new data, requiring a computational-expensive fine-tuning process. In this work, we introduce a Retrieval-augmented Visual Language Model, Re-ViLM, built upon the Flamingo, that supports retrieving the relevant knowledge from the external database for zero and in-context few-shot image-to-text generations. By storing certain knowledge explicitly in the external database, our approach reduces the number of model parameters and can easily accommodate new data during evaluation by simply updating the database. We also construct an interleaved image and text data that facilitates in-context few-shot learning capabilities. We demonstrate that Re-ViLM significantly boosts performance for image-to-text generation tasks, especially for zero-shot and few-shot generation in out-of-domain settings with 4 times less parameters compared with baseline methods.