A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge (2206.01718v1)

Abstract: The Visual Question Answering (VQA) task aspires to provide a meaningful testbed for the development of AI models that can jointly reason over visual and natural language inputs. Despite a proliferation of VQA datasets, this goal is hindered by a set of common limitations. These include a reliance on relatively simplistic questions that are repetitive in both concepts and linguistic structure, little world knowledge needed outside of the paired image, and limited reasoning required to arrive at the correct answer. We introduce A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer. In contrast to the existing knowledge-based VQA datasets, the questions generally cannot be answered by simply querying a knowledge base, and instead require some form of commonsense reasoning about the scene depicted in the image. We demonstrate the potential of this new dataset through a detailed analysis of its contents and baseline performance measurements over a variety of state-of-the-art vision-LLMs. Project page: http://a-okvqa.allenai.org/

An Analysis of A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge

The paper "A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge" addresses several significant challenges in the domain of Visual Question Answering (VQA) by introducing a novel dataset, A-OKVQA. VQA tasks require artificial intelligence models to process and reason over both visual and textual information while integrating external world knowledge. Despite many datasets in this space, there are persistent limitations that hinder comprehensive evaluation of AI capabilities. These limitations typically include simplistic question formation, over-reliance on image-contained knowledge, and deficient reasoning requirements.

Contributions and Dataset Characteristics

A-OKVQA comprises approximately 25,000 questions that necessitate diverse commonsense and world knowledge, representing a substantial scale of inquiry beyond simple knowledge retrieval. This dataset's questions cannot merely be answered through lookup queries in knowledge bases; they require reasoning with integrated visual cues. This complexity positions A-OKVQA as a significant extension over prior works like OK-VQA. The dataset incorporates multiple-choice and direct answer formats, with rationales included to outline the reasoning necessary for answers, providing a foundation for developing models that emphasize explanation alongside answer accuracy.

Comparative Evaluation and Analysis

When juxtaposed with existing datasets, A-OKVQA emphasizes knowledge domains ranging from explicit fact-based knowledge to the commonsense understanding of social behaviors, physics, and visual context. The research delineates a comparison of A-OKVQA’s properties with datasets like VCR and OK-VQA. A-OKVQA is distinguished by its larger size, variety in required knowledge, and annotated rationales that enhance the analysis of AI performance.

Methodological Evaluation

The paper evaluates several state-of-the-art vision-LLMs against A-OKVQA, including both pre-trained architectures and those specifically designed for knowledge-based VQA. Models such as GPT-3, ClipCap, and KRISP are assessed on their ability to utilize external reasoning capabilities. The analysis identifies that while some models perform admirably on the dataset, there is a conspicuous gap in reasoning capabilities, highlighting the need for innovation in AI understanding and deduction abilities.

Implications and Future Directions

The implementation of the A-OKVQA dataset facilitates deeper inquiry into AI's ability to generalize knowledge across domains, propose reasoning paths, and produce human-interpretable answers. As the findings indicate substantial variation in models' handling of complex reasoning scenarios, A-OKVQA stands as a challenging benchmark for exploring multi-modal intelligence and the integration of diverse knowledge systems in AI.

Future research may pivot to exploiting rationales for model improvements, developing hybrid architectures capable of leveraging structured knowledge, and refining techniques for commonsense reasoning. The A-OKVQA prompts exploration into holistic vision-language systems with greater world-aware reasoning capacities that mirror human cognitive integration of visual and textual inputs.

In sum, A-OKVQA represents a significant stride toward developing VQA systems that are not merely perceptive but also insightful and explanatory, paving paths for comprehensive advancements in AI-driven understanding tasks.