The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

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 …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Low-light image enhancement plays very important roles in low-level vision areas. Recent
works have built a great deal of deep learning models to address this task. However, these …

We present FasterSeg, an automatically designed semantic segmentation network with not
only state-of-the-art performance but also faster speed than current methods. Utilizing neural …

Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Over the past decade, deep learning models have exhibited considerable advancements,
reaching or even exceeding human-level performance in a range of visual perception tasks …

The compression of Generative Adversarial Networks (GANs) has lately drawn attention,
due to the increasing demand for deploying GANs into mobile devices for numerous …

This paper introduces an innovative approach to Wind Turbine Blade (WTB) inspection
through the synergistic use of thermal and RGB imaging, coupled with advanced deep …

Nowadays, driven by the high demand for autonomous driving and surveillance, infrared
and visible image fusion (IVIF) has attracted significant attention from both the industry and …