REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory (2212.05221v2)

Abstract: In this paper, we propose an end-to-end Retrieval-Augmented Visual LLM (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning.

Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory

This paper presents a pioneering approach to enhance the capabilities of visual-LLMs by integrating retrieval-augmented techniques. Specifically, it introduces 'e,' an end-to-end retrieval-augmented visual-LLM that utilizes a large-scale memory system to store and retrieve multimodal world knowledge. The primary objective is to improve the model's performance in answering knowledge-intensive queries.

Key Components

The model architecture consists of four integral components:

1. Memory: This component stores various sources of multimodal data, such as image-text pairs, question-answer pairs, and knowledge graph triplets. The memory is encoded uniformly through a specialized encoder.
2. Encoder: It processes the incoming multimodal data to produce a representation suitable for retrieval tasks.
3. Retriever: This component is responsible for identifying the most relevant entries in the memory based on the input query.
4. Generator: It synthesizes the retrieved knowledge with the input query to generate the final output. This is crucial for tasks such as visual question answering and image captioning.

Novelty and Methodology

The innovation in this paper lies in the unified pre-training of the memory, encoder, retriever, and generator components in an end-to-end manner. By training these components collectively on a vast dataset, the model benefits from a shared understanding of the multimodal data. Moreover, leveraging diverse knowledge sources has demonstrated substantial improvements in performance metrics.

Experimental Results

The experimental analysis indicates that 'e' achieves state-of-the-art results in both visual question answering and image captioning. The model effectively integrates multiple sources of data, thereby surpassing contemporary models constrained to singular knowledge types.

Implications and Future Prospects

The practical implications of this research are significant, offering advancements in systems requiring extensive knowledge integration, such as automated image captioning and complex query answering. Theoretically, this approach provides insights into multimodal representation learning and its potential to generalize across disparate tasks. Future developments in AI might explore expanding this retrieval-augmented approach to incorporate even more diverse data streams, paving the way for increasingly capable multimodal systems.

This work represents a critical step forward in leveraging retrieval mechanisms within visual-LLMs, highlighting the importance of integrating comprehensive multimodal data in enhancing AI's contextual awareness and reasoning abilities.