文件名称：CVPR2020论文.rar
文件大小：109.07MB
文件格式：RAR
更新时间：2023-04-01 04:22:00
CVPR2020 AI 机器学习 NLP 计算机视觉
CVPR2020致力于计算机视觉和模式识别包括颜色检测、跟踪、运动、物体识别、音响和目标检测。 包含了：分段和分组、运动和跟踪、人类的认识、Shape-from-X、音响和结构与运动、颜色和纹理、照明和反射建模、基于图像的建模、传感器、形状表示和匹配、计算摄影和视频、早期和生物启发的愿景、视频分析和事件识别、优化方法、脸和姿态分析、视频监控、现场了解、图像和视频检索、医学图像分析、对机器人的愿景、对图形的愿景、统计方法和学习、计算机视觉的应用、文档分析、对象识别、检测、分类。
【文件预览】：
CVPR2020论文
----Distribution-Aware Coordinate Representation for Human Pose Estimation.pdf(1.38MB)
----GhostNet More Features from Cheap Operations.pdf(1.42MB)
----The Devil is in the Details Delving into Unbiased Data Processing for Human Pose Estimation.pdf(867KB)
----What it Thinks is Important is Important Robustness Transfers through Input Gradients.pdf(1.62MB)
----In Perfect Shape Certifiably Optimal 3D Shape Reconstruction from 2D Landmarks.pdf(25.85MB)
----CVPR2020.rar(33.55MB)
----MSG-GAN Multi-Scale Gradients for Generative Adversarial Networks.pdf(9.15MB)
----Improved Few-Shot Visual Classification.pdf(3.22MB)
----Suppressing Uncertainties for Large-Scale Facial Expression Recognition.pdf(3.43MB)
----PolarMaskSingle Shot Instance Segmentation with Polar Representation.pdf(2.82MB)
----AdderNet Do We Really Need Multiplications in Deep Learning.pdf(619KB)
----Making Better Mistakes Leveraging Class Hierarchies with Deep Networks.pdf(873KB)
----CARS Contunuous Evolution for Efficient Neural Architecture Search.pdf(641KB)
----Your Local GAN Designing Two Dimensional Local Attention Mechanisms for Generative Models.pdf(5.25MB)
----xMUDA Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation.pdf(7.41MB)
----12-in-1 Multi-Task Vision and Language Representation Learning.pdf(7.09MB)
----Learning multiview 3D point cloud registration.pdf(8.6MB)
----Action Modifiers Learning from Adverbs in Instructional Videos.pdf(3.94MB)
----RoutedFusion Learning Real-time Depth Map Fusion (2).pdf(6.22MB)
----Multi-Modal Domain Adaptation for Fine-Grained Action Recognition.pdf(6.62MB)
----ROAM Recurrently Optimizing Tracking Model.pdf(2.88MB)
----RoutedFusion Learning Real-time Depth Map Fusion.pdf(6.22MB)