mlsanityData to 7 Oct 2026

FiLM: Visual Reasoning with a General Conditioning Layer

arXiv 1709.07871 ↗First posted 22 Sep 2017cs.CV cs.AI cs.CLcited by 2,286

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Cited by 420 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 2,286 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2017.

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Citing papers per year: 2018: 2; 2019: 1; 2020: 1; 2021: 154; 2022: 176; 2023: 224; 2024: 322; 2025: 496; 2026: 910.

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