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Visual QA is a pivotal challenge for higher-level reasoning, requiringunderstanding language, vision, and relationships between many objects in ascene. Although datasets like CLEVR are designed to be unsolvable without suchcomplex relational reasoning, some surprisingly simple feed-forward, "holistic"models have recently shown strong performance on this dataset. These modelslack any kind of explicit iterative, symbolic reasoning procedure, which arehypothesized to be necessary for counting objects, narrowing down the set ofrelevant objects based on several attributes, etc. The reason for this strongperformance is poorly understood. Hence, our work analyzes such models, andfinds that minor architectural elements are crucial to performance. Inparticular, we find that \textit{early fusion} of language and vision provideslarge performance improvements. This contrasts with the late fusion approachespopular at the dawn of Visual QA. We propose a simple module we call MultimodalCore, which we hypothesize performs the fundamental operations for multimodaltasks. We believe that understanding why these elements are so important tocomplex question answering will aid the design of better-performing algorithmsfor Visual QA while minimizing hand-engineering effort.

The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR