6 citations · 30 across the 12 of their papers we have counts for
9 papers · 1 filter
DiffRate : Differentiable Compression Rate for Efficient Vision Transformers
Mengzhao Chen, Wenqi Shao, Peng Xu +6
Token compression aims to speed up large-scale vision transformers (e.g. ViTs) by pruning (dropping) or merging tokens. It is an important but challenging task. Although recent adv…
Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Yanjing Li, Sheng Xu, Mingbao Lin +4
Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices. Fully-binarized ViTs (Bi-ViT) th…
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution Networks
Yunshan Zhong, Mingbao Lin, Jingjing Xie +3
This paper introduces Distribution-Flexible Subset Quantization (DFSQ), a post-training quantization method for super-resolution networks. Our motivation for developing DFSQ is bas…
Q-DETR: An Efficient Low-Bit Quantized Detection Transformer
Sheng Xu, Yanjing Li, Mingbao Lin +4
The recent detection transformer (DETR) has advanced object detection, but its application on resource-constrained devices requires massive computation and memory resources. Quanti…
Bi-directional Masks for Efficient N:M Sparse Training
Yuxin Zhang, Yiting Luo, Mingbao Lin +4
We focus on addressing the dense backward propagation issue for training efficiency of N:M fine-grained sparsity that preserves at most N out of M consecutive weights and achieves…
Real-Time Image Demoireing on Mobile Devices
Yuxin Zhang, Mingbao Lin, Xunchao Li +7
Moire patterns appear frequently when taking photos of digital screens, drastically degrading the image quality. Despite the advance of CNNs in image demoireing, existing networks…