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Efficient ViTs

Increasing the efficiency of ViTs without the modification of the architecture. (i.e., Key & Query Sparsification, Token pruning & merging)

Papers

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Showing 11–20 of 32 papers

TitleStatusHype
Making Vision Transformers Efficient from A Token Sparsification ViewCode1
Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer InferenceCode1
Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-AttentionCode1
Not All Patches are What You Need: Expediting Vision Transformers via Token ReorganizationsCode1
SPViT: Enabling Faster Vision Transformers via Soft Token PruningCode1
AdaViT: Adaptive Tokens for Efficient Vision TransformerCode1
Adaptive Token Sampling For Efficient Vision TransformersCode1
Pruning Self-attentions into Convolutional Layers in Single PathCode1
Global Vision Transformer Pruning with Hessian-Aware SaliencyCode1
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision TransformerCode1
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