Papers › FiLM: Visual Reasoning with a General Conditioning Layer

FiLM: Visual Reasoning with a General Conditioning Layer

22 Sep 2017arXiv:1709.07871archive 2025-07-28

Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron Courville

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.

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ethanjperez/film officialmentioned in paperpytorch report
CPJKU/audio_conditioned_unet mentioned on GitHubpytorch report
GuessWhatGame/clevr mentioned on GitHubtf report
caffeinism/film-pytorch mentioned on GitHubpytorch report
jjgo/hyperlight mentioned on GitHubpytorchApache-2.0 report
kdaip/stabletts mentioned on GitHubpytorch report
keonlee9420/Daft-Exprt mentioned on GitHubpytorch report

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FiLM ethanjperez/film/vr/models/filmed_net.py official repository ran licence not identified · pointer only · b8723266db5e5556 · report
Classifier caffeinism/film-pytorch/networks.py community (archive-listed) ran fingerprinted no licence file found · pointer only · 37dda49b6caaba23 · report
FeatureExtractor caffeinism/film-pytorch/networks.py community (archive-listed) ran fingerprinted no licence file found · pointer only · edbfcccbabd461c6 · report
FiLM keonlee9420/Daft-Exprt/model/blocks.py community (archive-listed) ran · metamorphic tier: invariant fingerprinted MIT (permissive) · bc5a2b1350c91ae2 · report
FiLM CPJKU/audio_conditioned_unet/audio_conditioned_unet/network.py community (archive-listed) ran MIT (permissive) · bd2e6b10e2f47993 · report
FiLMBlock caffeinism/film-pytorch/networks.py community (archive-listed) ran no licence file found · pointer only · 32874c063a45b37a · report
FiLMLayer kdaip/stabletts/models/estimator.py community (archive-listed) ran MIT (permissive) · 04d3db3a0eb24a12 · report
ResBlock caffeinism/film-pytorch/networks.py community (archive-listed) ran no licence file found · pointer only · 779ef9590306ac85 · report
conv caffeinism/film-pytorch/networks.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · b4a8caf47616fa06 · report
FiLM caffeinism/film-pytorch/networks.py community (archive-listed) unverified no licence file found · pointer only · 6d21918b730bc046 · report

Tasks

Image Retrieval with Multi-Modal QueryVisual Question Answering (VQA)Visual Question Answering (VQA) Split AVisual Question Answering (VQA) Split BVisual Reasoning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Retrieval with Multi-Modal Query MIT-States FiLM Recall@1 10.1 #4 of 5 Archive leaderboard report
Image Retrieval with Multi-Modal Query MIT-States FiLM Recall@10 38.3 #4 of 5 Archive leaderboard report
Image Retrieval with Multi-Modal Query MIT-States FiLM Recall@5 27.7 #4 of 5 Archive leaderboard report
Visual Question Answering (VQA) CLEVR CNN+GRU+FiLM Accuracy 97.7 #11 of 15 Archive leaderboard report
Visual Question Answering (VQA) CLEVR-Humans CNN+GRU+FiLM Accuracy 75.9 #3 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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