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FiLM: Visual Reasoning with a General Conditioning Layer
Open accessAbstract
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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“While MI-VECTOR (i.e., diagonal parameters in a MI layer which results in a vector output) corresponds to the most basic implementation of FILM, the original FILM layer uses multiple non-linear layers instead of a single linear transformation in MI-VECTOR which has been shown to improve performance [120].”
From MultiBench: Multiscale Benchmarks for Multimodal Representation Learning (2021) · cites this paper as [120]
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“By appropriately constraining the rank and structure of these parameters, MI recovers HyperNetworks [61] (unconstrained parameters resulting in a matrix output), Feature-wise linear modulation (FiLM) [120, 188] (diagonal parameters resulting in vector output), and Sigmoid units [37] (scalar parameters resulting in scalar output).”
From MultiBench: Multiscale Benchmarks for Multimodal Representation Learning (2021) · cites this paper as [120]
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Assisted Sound Sample Generation with Musical Conditioning in Adversarial Auto-EncodersPreprint
Bitton, A., Esling, P., Caillon, A., & Fouilleul, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.06215 · Cited by 9
Learning Visual Question Answering by Bootstrapping Hard AttentionPreprint
Malinowski, M., Doersch, C., Santoro, A., & Battaglia, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.00300 · Cited by 12
Group Equivariant Generative Adversarial NetworksPreprint
Dey, N., Chen, A., & Ghafurian, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.01683 · Cited by 9
Learning to Read and Follow Music in Complete Score Sheet ImagesPreprint
Henkel, F., Kelz, R., & Widmer, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10736 · Cited by 9
Content based singing voice source separation via strong conditioning using aligned phonemesPreprint
Meseguer-Brocal, G., & Peeters, G. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02070 · Cited by 9
Gaussian Gated Linear NetworksPreprint
Budden, D., Marblestone, A. H., Sezener, E., Lattimore, T., Wayne, G., & Veness, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05964 · Cited by 10
Few-shot Sequence Learning with TransformersPreprint
Logeswaran, L., Lee, A., Ott, M., Lee, H., Ranzato, M., & Szlam, A. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09543 · Cited by 11
Efficient Neural Networks for Real-time Analog Audio Effect Modeling.Preprint
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PixArt-$α$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
Chen, J., Yu, J., Ge, C., Yao, L., Xie, E., Wu, Y., Wang, Z., Kwok, J., Luo, P., Lu, H., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.00426 · Cited by 30
“We find that the linear projections in the adaptive normalization layers (Perez et al., 2018) ( adaLN ) module of the DiT account for a substantial proportion (27”
BackgroundVisually Guided Sound Source Separation With Audio-Visual Predictive Coding
Song, Z., & Zhang, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3288022 · Cited by 8
Chen, C., & Zhang, P. (2024) · Applied Acoustics · doi:10.1016/j.apacoust.2024.110169 · Cited by 8
Metadata
- Authors
- Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A.
- Affiliations
- Université de Montréal (CA)
- Centre National de la Recherche Scientifique (FR)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-29
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2760103357
- Cited by
- 2,490
- References
- 55
- Citations by year
- 2026: 823 · 2025: 412 · 2024: 280 · 2023: 221 · 2022: 158 · 2021: 244 · 2020: 179 · 2019: 120 · 2018: 52
- FWCI
- 44.92
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- Nvidia
- Fonds de recherche du Québec – Nature et technologies
- CHIST-ERA
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- cinema, feature-wise linear modulation, visual reasoning, visual question answering, feature modulation, zero-shot generalization, few-shot learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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