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-theart 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. Index Terms: Deep Learning, Language and Vision 1. FiLM models achieve state-of-the-art across a variety of visual reasoning tasks, often by significant margins. 2. FiLM operates in a coherent manner. It learns a complex, underlying structure and manipulates the conditioned network's features in a selective manner. It also enables the CNN to properly localize question-referenced objects. 3. FiLM is robust; many FiLM model ablations still outper-arXiv:1709.07871v1 [cs.CV]
Perez et al. REF develop a feature-wise linear modulation layer (FiLM), to exploit linguistic information for visual reasoning.
FiLM: Visual Reasoning with a General Conditioning Layer
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