Papers › FiLM: Visual Reasoning with a General Conditioning Layer
FiLM: Visual Reasoning with a General Conditioning Layer
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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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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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