FiLM: Visual Reasoning with a General Conditioning Layer

Abstract

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.

Keywords

Affiliated Institutions

• Centre National de la Recherche Scientifique FR
• Université de Lille FR
• Université de Montréal CA
• Rice University US
• Centre Universitaire de Mila DZ
• Canadian Institute for Advanced Research CA
• École Centrale de Lille FR

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