ARL2 replaces quadratic cross-frame attention in AR video diffusion with a fixed-size recurrent state, achieving linear-time scaling and constant memory while preserving quality.
International Conference on Machine Learning , year=
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Pith papers citing it
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cs.CV 2years
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UNVERDICTED 2representative citing papers
LaplacianFormer uses a Laplacian kernel with an injective feature map and efficient approximations to achieve linear attention that preserves mid-range interactions better than Gaussian-based linear attention in vision transformers.
citing papers explorer
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Attend Locally, Remember Linearly: Linear Attention as Cross-Frame Memory for Autoregressive Video Diffusion
ARL2 replaces quadratic cross-frame attention in AR video diffusion with a fixed-size recurrent state, achieving linear-time scaling and constant memory while preserving quality.
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LaplacianFormer:Rethinking Linear Attention with Laplacian Kernel
LaplacianFormer uses a Laplacian kernel with an injective feature map and efficient approximations to achieve linear attention that preserves mid-range interactions better than Gaussian-based linear attention in vision transformers.