REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering (2206.01201v2)

Abstract: This paper revisits visual representation in knowledge-based visual question answering (VQA) and demonstrates that using regional information in a better way can significantly improve the performance. While visual representation is extensively studied in traditional VQA, it is under-explored in knowledge-based VQA even though these two tasks share the common spirit, i.e., rely on visual input to answer the question. Specifically, we observe that in most state-of-the-art knowledge-based VQA methods: 1) visual features are extracted either from the whole image or in a sliding window manner for retrieving knowledge, and the important relationship within/among object regions is neglected; 2) visual features are not well utilized in the final answering model, which is counter-intuitive to some extent. Based on these observations, we propose a new knowledge-based VQA method REVIVE, which tries to utilize the explicit information of object regions not only in the knowledge retrieval stage but also in the answering model. The key motivation is that object regions and inherent relationship are important for knowledge-based VQA. We perform extensive experiments on the standard OK-VQA dataset and achieve new state-of-the-art performance, i.e., 58.0% accuracy, surpassing previous state-of-the-art method by a large margin (+3.6%). We also conduct detailed analysis and show the necessity of regional information in different framework components for knowledge-based VQA. Code is publicly available at https://github.com/yzleroy/REVIVE.

An Expert Analysis of "REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering"

The paper "REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering" introduces an innovative methodology to address the challenge of effectively leveraging visual information in knowledge-based Visual Question Answering (VQA). This paper critically revisits the role of visual representation and underscores the significance of incorporating regional information to enhance the precision of answering knowledge-based queries stemming from visual inputs. The framework developed, named REVIVE, systematically explores and utilizes object regions not only during the knowledge retrieval phase but also within the answering model itself.

Overview of Methodology

The research identifies a gap in the utilization of visual data in existing state-of-the-art knowledge-based VQA models. These models predominantly rely on either global image features or sliding windows for knowledge retrieval, often neglecting intricate object-level relationships. REVIVE builds on this understanding by intensively focusing on object-centric visual representations to improve both the knowledge retrieval process and the answering model.

REVIVE operates by extracting object regions using a robust object detector (GLIP), followed by utilizing these regions for knowledge retrieval and answer generation. Specifically, the method retrieves explicit external knowledge from a subset of Wikidata and implicit knowledge through prompts to the GPT-3 LLM. The integration of these knowledge resources with detailed regional visual representations in a transformer-based architecture forms the core of the proposed methodology.

Performance Evaluation and Results

The effectiveness of REVIVE is empirically validated using the OK-VQA dataset. The paper reports a substantial leap in accuracy, achieving a 58.0% accuracy compared to the previous best method at 54.4%. This performance improvement is attributed to effectively harnessing regional visual features to enhance both knowledge retrieval and answer decoding. The research presents extensive analyses demonstrating the critical role regional information plays across different components of the proposed framework.

Implications and Future Directions

REVIVE's contribution lies in demonstrating the untapped potential of regional visual representations in knowledge-based VQA, where a more intricate understanding of visual details can significantly uplift the answer precision. The model's design lays a foundation for future research exploring deeper integration of visual nuances into VQA systems. As artificial intelligence continues to evolve, enhancing machine understanding and reasoning through improved visual representation strategies such as REVIVE could see broader applications in complex decision-making systems, educational technologies, interactive AI services, and beyond.

Future research could further explore the scalability of REVIVE across diverse datasets and real-world applications, potentially integrating more sophisticated models that consider temporal or sequential visual data. Additionally, understanding and mitigating the potential biases in implicit knowledge sources remains an open avenue for researchers.

In conclusion, the research presents a noteworthy advancement in the field of knowledge-based VQA by demonstrating how regional visual representation, when methodically harnessed, can address existing challenges and significantly improve model performance.