1. Introduction
Vision-Language Models (VLMs) have made significant strides in multimodal understanding, yet they often struggle with complex reasoning or requiring external knowledge. This limitation restricts their performance in scenarios demanding deep contextual understanding or factual recall. This work proposes to address this by integrating an explicit knowledge generation step within the VLM inference process, enhancing their ability to leverage internal representations effectively. The models used in this article include a base VLM (e.g., LLaVA-1.5, InstructBLIP), a knowledge generation module (e.g., a fine-tuned GPT-3.5 or LLaMA-2), and an answer generation module utilizing the base VLM augmented with knowledge.
2. Related Work
Prior research has explored various methods to enhance VLM capabilities, including architectural improvements, advanced training techniques, and external knowledge integration. Approaches such as retrieval-augmented generation (RAG) for language models have shown promise, but their direct application to VLMs often faces challenges in seamless multimodal knowledge retrieval. Other methods focus on advanced prompting strategies, yet they typically rely on human-curated prompts. Our work distinguishes itself by enabling VLMs to autonomously generate task-specific knowledge, moving beyond static knowledge bases or fixed prompting schemes.
3. Methodology
The proposed 'Look, Recite, Then Answer' framework operates in three distinct phases. First, the VLM 'Looks' at the visual input, generating an initial understanding. Second, a specialized 'Recite' module, trained to extract or infer relevant information, generates knowledge hints in natural language based on the visual input and the task prompt. Finally, the VLM 'Answers' the original query by incorporating both the visual features and the self-generated knowledge hints, guiding its response generation. This modular design allows for flexible integration with various existing VLM architectures, effectively augmenting their reasoning capabilities.
4. Experimental Results
Experiments were conducted on several benchmark datasets, including VQAv2, GQA, and COCO Captioning, comparing the proposed framework against baseline VLMs. Our results consistently show significant improvements in accuracy, particularly on complex reasoning tasks and those requiring common-sense knowledge. For instance, on VQAv2, the 'Look, Recite, Then Answer' approach achieved a 3.5% absolute increase in overall accuracy compared to the baseline VLM. The table below summarizes key performance metrics across different datasets.
Explanation of Results: The table clearly indicates that integrating self-generated knowledge hints significantly boosts performance across diverse VLM tasks. The 'Look, Recite, Then Answer' model consistently outperforms the baseline, especially in VQA, demonstrating its enhanced reasoning capabilities. This improvement highlights the effectiveness of using explicit knowledge generation to guide VLM predictions.
| Dataset | Metric | Baseline VLM | Look, Recite, Then Answer |
|---|---|---|---|
| VQAv2 | Accuracy (%) | 72.1 | 75.6 |
| GQA | Accuracy (%) | 60.5 | 63.9 |
| COCO Captioning | CIDEr | 115.3 | 119.8 |
5. Discussion
The experimental findings strongly support the efficacy of self-generated knowledge hints in augmenting VLM performance, particularly for tasks demanding deeper understanding and reasoning. The 'Recite' phase effectively bridges the gap between raw visual perception and nuanced linguistic generation, providing a more robust contextual foundation. While the current framework shows promise, future work could explore dynamic hint generation tailored to specific task complexities or investigate the integration of external real-time knowledge sources. This approach paves the way for more autonomous and intelligent multimodal AI systems.