1 citations · 1 across the 14 of their papers we have counts for
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QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models
Tien-Yu Chi, Hung-Yueh Chiang, Diana Marculescu +1
State space models (SSMs) reduce the quadratic complexity of transformers by leveraging linear recurrence. Recently, VMamba has emerged as a strong SSM-based vision backbone, yet r…
V"Mean"ba: Visual State Space Models only need 1 hidden dimension
Tien-Yu Chi, Hung-Yueh Chiang, Chi-Chih Chang +2
Vision transformers dominate image processing tasks due to their superior performance. However, the quadratic complexity of self-attention limits the scalability of these systems a…
ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration
Ning-Chi Huang, Chi-Chih Chang, Wei-Cheng Lin +3
sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing $N{…
FLORA: Fine-grained Low-Rank Architecture Search for Vision Transformer
Chi-Chih Chang, Yuan-Yao Sung, Shixing Yu +3
Vision Transformers (ViT) have recently demonstrated success across a myriad of computer vision tasks. However, their elevated computational demands pose significant challenges for…