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A survey of model compression strategies for object detection
Z Lyu, T Yu, F Pan, Y Zhang, J Luo, D Zhang… - Multimedia tools and …, 2024 - Springer
Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …
However, such DNNS-based large object detection models are generally computationally …
Image recognition based on lightweight convolutional neural network: Recent advances
Y Liu, J Xue, D Li, W Zhang, TK Chiew, Z Xu - Image and Vision Computing, 2024 - Elsevier
Image recognition is an important task in computer vision with broad applications. In recent
years, with the advent of deep learning, lightweight convolutional neural network (CNN) has …
years, with the advent of deep learning, lightweight convolutional neural network (CNN) has …
Pruning parameterization with bi-level optimization for efficient semantic segmentation on the edge
With the ever-increasing popularity of edge devices, it is necessary to implement real-time
segmentation on the edge for autonomous driving and many other applications. Vision …
segmentation on the edge for autonomous driving and many other applications. Vision …
HALOC: hardware-aware automatic low-rank compression for compact neural networks
Low-rank compression is an important model compression strategy for obtaining compact
neural network models. In general, because the rank values directly determine the model …
neural network models. In general, because the rank values directly determine the model …