FA-SSD中我们将2阶残差注意力改为1阶残差注意力,但文章并没说1阶残差注意力到底是啥玩意。1阶注意力放在conv4和conv7后,并且与conv7和conv8,conv8和conv9融合。也就是说将conv4与conv7都选为target layer。而FSSD中只将conv4作为target layer。 4 Experiments 4.1 Experimental setup 4.2 Ablation studies 表...
Small Object Detection using Context and Attention 论文阅读笔记 出处:2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Jeju Island, Korea 一、介绍 目标检测算法在各种环境下的应用存在许多局限性,特别是检测小物体仍然具有挑战性,因为它们的分辨率低,信息有限。 文章...
Small object detectionContextual informationFeature enhancementAttentionPlug-and-play modulesDetecting small objects is a challenging task in computer vision due to the objects only occupying a limited number of pixels and having blurred contours. These factors result in minimal discriminative features being...
HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection 解释HCF-Net是什么: HCF-Net(Hierarchical Context Fusion Network)是一种专为红外小目标检测设计的深度学习模型。它旨在通过融合不同层次的上下文信息来提高红外图像中小目标的检测性能。HCF-Net通过构建一个多层次的特征提取和融合框...
There are many limitations applying object detection algorithm on various environments. Especially detecting small objects is still challenging because they have low resolution and limited information. We propose an object detection method using context for improving accuracy of detecting small objects. The...
我们的Container架构在ImageNet上使用22M参数实现了82.7%的Top-1精度,比DeiT-Small提高了2.8,并且可以在短短200个时代收敛到79.9%的Top-1精度。比起相比的基于Transformer的方法不能很好地扩展到下游任务依赖较大的输入图像的分辨率,我们高效的网络,名叫CONTAINER-LIGHT,可以使用在目标检测和分割网络如DETR实例,RetinaNet...
Contextual information in complex scenarios is critical for accurate object detection. Existing state-of-the-art detectors have greatly improved detection performance with the use of contexts around objects. However, these detectors consider the local and global contexts separately, which limits the improv...
我们的CONTAINER架构在ImageNet上使用22M参数实现了82.7%的Top-1准确率,相比DeiT-Small提高了2.8个百分点,并且只需200个epoch就能收敛到79.9%的Top-1准确率。与基于Transformer的方法相比,后者在依赖更大输入图像分辨率的下游任务中不具备良好的扩展性,我们的高效网络CONTAINER-LIGHT可以应用于DETR、RetinaNet和Mask-RCNN...
The experimental results show that our proposed detector achieves better results than DSOD and exceeds most of the existing excellent detectors, especially detects partially occluded objects and small objects well.doi:10.1007/s11042-020-09500-6Jingjuan Guo...
谷歌提出了一种目标检测的新方法Context R-CNN,简单地说,就是利用摄像头长时间的拍摄内容,推理出模糊画面里的目标。这种模型的性能优于单帧Faster R-CNN。 这种新的对象检测体系结构利用网络中每个摄像机在整个时间范围内的上下文线索,无需依赖大量摄像机的额外训练数据,即可提高对目标的识别能力。