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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 21–30 of 32 papers

TitleStatusHype
Training data-efficient image transformers & distillation through attentionCode1
PPT: Token Pruning and Pooling for Efficient Vision TransformersCode1
Scalable Vision Transformers with Hierarchical PoolingCode1
Pruning Self-attentions into Convolutional Layers in Single PathCode1
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision TransformerCode1
M^2-ViT: Accelerating Hybrid Vision Transformers with Two-Level Mixed Quantization—0
An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT—0
IA-RED^2: Interpretability-Aware Redundancy Reduction for Vision Transformers—0
ImagePiece: Content-aware Re-tokenization for Efficient Image Recognition—0
Patch Slimming for Efficient Vision Transformers—0
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