【文件属性】：
文件名称：有关目标分类、目标检测的相关论文集合
文件大小：115.98MB
文件格式：RAR
更新时间：2022-04-21 16:33:07
目标分类 目标检测 论文
有关目标分类、目标检测的相关论文集合，包含有rcnn系列，ssd、yolo等
【文件预览】：
Deep-learing-references
----Deformable Convolutional Networks.pdf(6.63MB)
----Deep Residual Learning for Image Recognition.pdf(800KB)
----Yolo9000.pdf(5.01MB)
----Learning from Simulated and Unsupervised Images through Adversarial.pdf(1.66MB)
----MULTI-SCALE CONTEXT AGGREGATION BY.pdf(2.86MB)
----Learning Mid-Level Features For Recognition.pdf(214KB)
----2D Human Pose Estimation New Benchmark and State of the Art Analysis.pdf(774KB)
----Mask^X R-CNN.pdf(5.88MB)
----Mask R-CNN.pdf(7.34MB)
----Bayesian GAN.pdf(6.65MB)
----resnet.pdf(800KB)
----Alexnet.pdf(1.35MB)
----Light-Head R-CNN.pdf(2.15MB)
----BN.pdf(169KB)
----Computational Imaging on the Electric Grid.pdf(2.89MB)
----Feature Pyramid Networks for Object Detection.pdf(771KB)
----FPN.pdf(771KB)
----SSD.pdf(2.38MB)
----Panoptic Feature Pyramid Networks.pdf(3.41MB)
----Annotating Object Instances with a Polygon-RNN.pdf(3.4MB)
----YOLO LITE A Real-Time Object Detection.pdf(1.35MB)
----YOLOv3.pdf(2.14MB)
----Object retrieval with large vocabularies and fast spatial matching.pdf(1.46MB)
----Soft-Nms.pdf(2.11MB)
----Speedaccuracy trade-offs for modern convolutional object detectors.pdf(7.93MB)
----Network In Network.pdf(581KB)
----Yolo.pdf(1.21MB)
----R-CNN.pdf(6.23MB)
----residual-networks-behave-like-ensembles-of-relatively-shallow-networks.pdf(541KB)
----SPP-Net.pdf(2.2MB)
----Squeeze-and-Excitation Networks.pdf(2.33MB)
----R-FCN.pdf(8.62MB)
----Faster R-CNN.pdf(6.59MB)
----Fast R-CNN.pdf(714KB)
----Densely Connected Convolutional Networks.pdf(1.09MB)
----Res Net.pdf(7.93MB)
----Speed accuracy trade-offs for modern convolutional object detectors.pdf(7.93MB)
----DCGAN.pdf(7.11MB)
----3DCNN.pdf(2.01MB)
----ThunderNet Towards Real-time Generic Object Detection.pdf(4.26MB)
----Focal Loss for Dense Object Detection.pdf(1.22MB)
----Fully Convolutional Networks for Semantic Segmentation.pdf(2.62MB)
----learning-features-2009-TR.pdf(4.01MB)