GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering (1902.09506v3)

Abstract: We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages scene graph structures to create 22M diverse reasoning questions, all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate question biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. An extensive analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3%, offering ample opportunity for new research to explore. We strongly hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding for images and language.

Overview of GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering

The paper "GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering" by Drew A. Hudson and Christopher D. Manning presents a meticulously curated dataset designed to advance the field of visual reasoning and question answering. The GQA dataset is introduced as a significant contribution to the challenge of generating compositional, interpretable, and context-rich answers from visual data.

This academic paper positions GQA as a benchmark to address limitations observed in existing datasets like VQA, by emphasizing compositionality, real-world applicability, and detailed semantic understanding. The dataset consists of over 22 million questions paired with 140,000 images, structured to encourage models to exhibit sophisticated reasoning rather than simple pattern recognition. This dataset is characterized by a diversity of question types, involving object recognition, attribute categorization, and relational reasoning.

In the evaluation of models using GQA, the authors demonstrate the need for advanced reasoning capabilities. Results reveal significant disparities in accuracy across different question categories, underscoring the dataset’s capacity to expose and challenge areas where traditional visual question answering models may fall short. Importantly, the paper discusses the application of semantic analysis and scene graphs, which play a crucial role in understanding the compositional nature of the questions and the required reasoning.

The rigorous quantitative analysis offered in the paper highlights strong findings. For instance, it is noted that models often achieve high performance on object and attribute recognition but struggle with multi-step relational questions, presenting a gap between current capabilities and the demands posed by real-world visual reasoning tasks.

The paper further elaborates on the implications of the GQA dataset for the development of future AI systems. By providing a structured platform designed to enhance reasoning capabilities, this dataset paves the way for creating more generalizable and robust AI models. Researchers are encouraged to utilize GQA for developing models capable of nuanced understanding and incrementally improving the interpretability of AI systems.

Potential future developments spurred by the GQA dataset include advancements in integrating visual and textual information, improving scene understanding, and the refinement of architectures conducive to complex reasoning tasks. The dataset serves as a tool to bridge the gap towards achieving human-like visual understanding and reasoning in AI.

In conclusion, "GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering" provides an invaluable resource aimed at driving forward the capabilities of AI in visual question answering tasks. It offers a concrete challenge to the research community to develop models that transcend current limitations, fostering advancements that are theoretically profound and practically applicable.