FiLM
A conditional modulation mechanism proposed by Perez et al. in 2018. Its core idea is to use a conditioning signal, such as text, to generate scaling parameter γ and shift parameter β, applying a channel-wise affine transformation to target feature maps via FiLM(x) = γ·x + β. It was originally developed for visual question answering tasks.
Timeline (last 90 days)
The student model is a U-Net with 1.4 million parameters, equipped with a FiLM slider for dynamically controlling weather intensity at inference time, operating at a resolution of 512×288 with a single-frame inference time of approximately 26 milliseconds.
MDT employs a FiLM (Feature-wise Linear Modulation)-based text-conditioned modulation mechanism that allows text signals to modulate facial and speech features.
FiLM was proposed by Perez et al. in 2018, originally for visual question answering; its core idea is to use a conditioning signal to generate scaling parameter γ and shift parameter β to perform channel-wise affine transformation on features.
All Facts (3)
The student model is a U-Net with 1.4 million parameters, equipped with a FiLM slider for dynamically controlling weather intensity at inference time, operating at a resolution of 512×288 with a single-frame inference time of approximately 26 milliseconds.
50%UnverifiedMDT employs a FiLM (Feature-wise Linear Modulation)-based text-conditioned modulation mechanism that allows text signals to modulate facial and speech features.
50%UnverifiedFiLM was proposed by Perez et al. in 2018, originally for visual question answering; its core idea is to use a conditioning signal to generate scaling parameter γ and shift parameter β to perform channel-wise affine transformation on features.
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