Recently human–robot collaboration (HRC) has emerged as a promising paradigm for mass
personalization in manufacturing owing to the potential to fully exploit the strength of human …

We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …

Real-time object detection is one of the most important research topics in computer vision.
As new approaches regarding architecture optimization and training optimization are …

This paper introduces EfficientNetV2, a new family of convolutional networks that have faster
training speed and better parameter efficiency than previous models. To develop these …

The better accuracy and efficiency trade-off has been a challenging problem in object
detection. In this work, we are dedicated to studying key optimizations and neural network …

Image dehazing is a challenging task in computer vision. Currently, most dehazing methods
adopt the U-Net architecture that directly fuses the decoding layer with the corresponding …

Batch Normalization is a key component in almost all state-of-the-art image classifiers, but it
also introduces practical challenges: it breaks the independence between training examples …

HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous
attention by automating the design of DNNs deployed in more resource-constrained daily …

Neural Architecture Search (NAS) methods have been growing in popularity. These
techniques have been fundamental to automate and speed up the time consuming and error …

Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision
tasks. However, ViTs' self-attention module is still arguably a major bottleneck, limiting their …