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Visual Question Answering (VQA) has emerged as a Visual Turing Test tovalidate the reasoning ability of AI agents. The pivot to existing VQA modelsis the joint embedding that is learned by combining the visual features from animage and the semantic features from a given question. Consequently, a largebody of literature has focused on developing complex joint embedding strategiescoupled with visual attention mechanisms to effectively capture the interplaybetween these two modalities. However, modelling the visual and semanticfeatures in a high dimensional (joint embedding) space is computationallyexpensive, and more complex models often result in trivial improvements in theVQA accuracy. In this work, we systematically study the trade-off between themodel complexity and the performance on the VQA task. VQA models have a diversearchitecture comprising of pre-processing, feature extraction, multimodalfusion, attention and final classification stages. We specifically focus on theeffect of "multi-modal fusion" in VQA models that is typically the mostexpensive step in a VQA pipeline. Our thorough experimental evaluation leads usto two proposals, one optimized for minimal complexity and the other oneoptimized for state-of-the-art VQA performance.

Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models