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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 1–10 of 32 papers

TitleStatusHype
ImagePiece: Content-aware Re-tokenization for Efficient Image Recognition—0
M^2-ViT: Accelerating Hybrid Vision Transformers with Two-Level Mixed Quantization—0
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision TransformerCode0
An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT—0
Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision TransformersCode1
GTP-ViT: Efficient Vision Transformers via Graph-based Token PropagationCode1
PPT: Token Pruning and Pooling for Efficient Vision TransformersCode1
Learned Thresholds Token Merging and Pruning for Vision TransformersCode1
MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation DatasetsCode1
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision TransformerCode1
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