Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models
This addresses the computational inefficiency in VQA models for researchers and practitioners, but it is incremental as it focuses on optimizing existing multimodal fusion methods.
The paper systematically studies the trade-off between model complexity and performance in Visual Question Answering (VQA), finding that more complex models often yield trivial accuracy improvements, and proposes two models optimized for minimal complexity and state-of-the-art performance.
Visual Question Answering (VQA) has emerged as a Visual Turing Test to validate the reasoning ability of AI agents. The pivot to existing VQA models is the joint embedding that is learned by combining the visual features from an image and the semantic features from a given question. Consequently, a large body of literature has focused on developing complex joint embedding strategies coupled with visual attention mechanisms to effectively capture the interplay between these two modalities. However, modelling the visual and semantic features in a high dimensional (joint embedding) space is computationally expensive, and more complex models often result in trivial improvements in the VQA accuracy. In this work, we systematically study the trade-off between the model complexity and the performance on the VQA task. VQA models have a diverse architecture comprising of pre-processing, feature extraction, multimodal fusion, attention and final classification stages. We specifically focus on the effect of "multi-modal fusion" in VQA models that is typically the most expensive step in a VQA pipeline. Our thorough experimental evaluation leads us to two proposals, one optimized for minimal complexity and the other one optimized for state-of-the-art VQA performance.