Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Object detection and object recognition are the most important applications of computer
vision. To pursue the task of object detection efficiently, a model with higher detection …

利用侧扫声呐图像来探查海底目标对海洋资源开采和海上军事防护都有重大意义.
目前人为提取图像特征进行目标检测的传统机器学习方法逐渐被深度学习取代 …

In this paper we present Sniffer Faster R-CNN (SFR-CNN), a novel camera-LiDAR sensor
fusion framework for fast and accurate object detection in autonomous driving scenarios …

Small object detection is a fundamental and challenging issue in computer vision. We
believe that there are two factors that affect the performance of small object detection: small …

Feature extraction networks (FENs), as the first stage in many computer vision tasks, play
critical roles. Previous studies regarding FENs employed deeper and wider networks to …

In recent years, top referred methods on object detection like R-CNN have implemented this
task as a combination of proposal region generation and supervised classification on the …

Face detection is challenging in unconstrained environments, where it encounters various
challenges such as orientation, pose, and occlusion. Deep convolutional neural networks …

Channel pruning is an efficient technique for model compression, removing redundant parts
of a convolutional neural network with minor degradation in classification accuracy. Previous …

As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural
Networks have achieved an impressive success in solving many problems in several fields …