Results 81 to 90 of about 7,363,707 (395)
RGBD Salient Object Detection, Based on Specific Object Imaging
Sensors, 2022 RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects.
Xiaolian Liao +4 more
doaj +1 more source
CenterNet++ for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence11 pages, 9 figures, 8 tables.
Kaiwen Duan +5 more
openaire +3 more sources
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection [PDF]
Computer Vision and Pattern Recognition, 2019 We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.
Shaoshuai Shi +6 more
semanticscholar +1 more source
Auxiliary Detection Head for One-Stage Object Detection
IEEE Access, 2020 The auxiliary classifier can improve the performance of classification networks. However, the utility of the auxiliary detection head has not been explored in the object detection field.
Guozheng Jin +2 more
doaj +1 more source
Object Detection in Videos with Tubelet Proposal Networks
, 2017 Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset. Different from object detection in static images, temporal information in videos is vital for object detection.
Kang, Kai +6 more
core +1 more source
IEEE Transactions on Pattern Analysis and Machine IntelligenceObject detection, a fundamental and challenging problem in computer vision, has experienced rapid development due to the effectiveness of deep learning. The current objects to be detected are mostly rigid solid substances with apparent and distinct visual characteristics.
Kailai Zhou +4 more
openaire +3 more sources
Deep Learning for Generic Object Detection: A Survey [PDF]
International Journal of Computer Vision, 2018 Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.
Li Liu +6 more
semanticscholar +1 more source
Out-of-Distribution Detection for LiDAR-based 3D Object Detection [PDF]
arXiv, 2022 3D object detection is an essential part of automated driving, and deep neural networks (DNNs) have achieved state-of-the-art performance for this task. However, deep models are notorious for assigning high confidence scores to out-of-distribution (OOD) inputs, that is, inputs that are not drawn from the training distribution.
arxiv
Activity Driven Weakly Supervised Object Detection
, 2019 Weakly supervised object detection aims at reducing the amount of supervision required to train detection models. Such models are traditionally learned from images/videos labelled only with the object class and not the object bounding box.
Ghadiyaram, Deepti +4 more
core +1 more source