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cs.CV2024
Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers
Rui Ding, Liang Yong, Sihuan Zhao +4
Due to its efficiency, Post-Training Quantization (PTQ) has been widely adopted for compressing Vision Transformers (ViTs). However, when quantized into low-bit representations, th…
eess.IV2024
EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution
Xi Su, Xiangfei Shen, Mingyang Wan +4
Single hyperspectral image super-resolution (single-HSI-SR) aims to improve the resolution of a single input low-resolution HSI. Due to the bottleneck of data scarcity, the develop…
cs.CV2024
PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator
Shiguang Wang, Tao Xie, Haijun Liu +2
Channel Pruning is one of the most widespread techniques used to compress deep neural networks while maintaining their performances. Currently, a typical pruning algorithm leverage…