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)
HypTI designs an adaptive fusion module that generates subject-specific scale and shift coefficients to dynamically modulate and integrate multimodal embeddings, with a mechanism similar to the FiLM idea.
Conditioned feature modulation in hyperbolic space needs to be performed in the tangent space or via exponential/logarithmic maps in order to preserve the geometric structure.
Ablation experiments show that FiLM does almost all of the effective work, while SelfAttention and MemoryCrossAttention contribute close to zero in the trained model.
In terms of network structure, a CNN processes a 21x21 local observation window, an LSTM carries memory across steps, and three attention modules—SelfAttention, MemoryCrossAttention and FiLM—are stacked on top of the CNN.
FiLM is a conditional normalization technique that uses an auxiliary network to generate a scaling coefficient γ and an offset coefficient β based on context, applying an affine transformation to each channel of the main feature map: output = γ · feature + β.
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.
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.
MDT employs a FiLM (Feature-wise Linear Modulation)-based text-conditioned modulation mechanism that allows text signals to modulate facial and speech features.
All Facts (8)
HypTI designs an adaptive fusion module that generates subject-specific scale and shift coefficients to dynamically modulate and integrate multimodal embeddings, with a mechanism similar to the FiLM idea.
50%UnverifiedConditioned feature modulation in hyperbolic space needs to be performed in the tangent space or via exponential/logarithmic maps in order to preserve the geometric structure.
50%UnverifiedAblation experiments show that FiLM does almost all of the effective work, while SelfAttention and MemoryCrossAttention contribute close to zero in the trained model.
50%UnverifiedIn terms of network structure, a CNN processes a 21x21 local observation window, an LSTM carries memory across steps, and three attention modules—SelfAttention, MemoryCrossAttention and FiLM—are stacked on top of the CNN.
50%UnverifiedFiLM is a conditional normalization technique that uses an auxiliary network to generate a scaling coefficient γ and an offset coefficient β based on context, applying an affine transformation to each channel of the main feature map: output = γ · feature + β.
50%UnverifiedThe 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%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.
50%UnverifiedMDT employs a FiLM (Feature-wise Linear Modulation)-based text-conditioned modulation mechanism that allows text signals to modulate facial and speech features.
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