Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we
present a new dataset as well as a new self-supervised learning method for ImageNet pre …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Abstract We present Sparse R-CNN, a purely sparse method for object detection in images.
Existing works on object detection heavily rely on dense object candidates, such as k anchor …

Learning medical visual representations directly from paired radiology reports has become
an emerging topic in representation learning. However, existing medical image-text joint …

Multi-exposure image fusion (MEF) targets to integrate multiple shots with different
exposures and generates a single higher dynamic image than each. Existing deep learning …

While class activation map (CAM) generated by image classification network has been
widely used for weakly supervised object localization (WSOL) and semantic segmentation …

摘要视觉目标检测旨在定位和识别图像中存在的物体, 属于计算机视觉领域的经典任务之一,
也是许多计算机视觉任务的前提与基础, 在自动驾驶, 视频监控等领域具有重要的应用价值 …

This paper investigates two techniques for developing efficient self-supervised vision
transformers (EsViT) for visual representation learning. First, we show through a …