Hence, instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. In this survey paper on instance segmentation -- its background, issues, techniques, evolution, popular datasets, related work up to the ...
增量学习领域有意思的工作很多,而且各有特点:比如Il2m: Class incremental learning with dual memory[13]还是通过记忆与增量参数调整的传统思路来做有类别增量的学习;Random path selection for incremental learning[14]使用一个比较大的模型,而通过面向不同数据激活不同神经元的方式,来给每一批数据一个神经路径,增强...
一些最近的目标检测系统,如 STDN,DSOD,TinyDSOD 以及 Pelee 都选择将 DenseNet 作为 backbone; Mask RCNN (instance segmentation 的 SOTA),将 ResNeXt (第二代 ResNet) 作为 backbone; 此外,为了能够加快检测速度,一些检测系统如 MobileNet 和 LightHead RCNN 中用到了 depth-wise separable convolution operation...
What are the pros and cons of each group of methods? 毕竟是survey,篇幅不长,要想真正做到in-depth,还是要自己看原文 3. A comprehensive analysis of detection speed up techniques: 这一部分我觉得很适合入门,没有涉及深度的数学推导,简单的介绍了渗漏卷积,网络压缩,模型蒸馏,以及integral image, vector qua...
Chen, K. et al.: Hybrid task cascade for instance segmentation. arXiv:1901.07518 (2019) Marechal, C. et al.: Survey on AI-based multimodal methods for emotion detection. In: High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet...
Deep Learning for 3D Point Clouds: A Survey 论文阅读 Abstract:在点云深度学习中,主要包含的任务有:3D形状分类、3D目标检测和跟踪、3D点云分割。 Introduction:3D数据通常有许多种表现形式:深度图、点云、网格、体积网格(volumetric grids)。点云表示的好处是:保持了最原始的3D空间中的几何信息,并且没有任何的...
MS-COCO 数据集是当前最具有挑战性的数据集,和 VOC 及 ILSVRC 相比,MS-COCO 中的物体都通过 per-instance segmentation 进行了进一步的标注,进而为 precise localization 提供了便利。此外,MS-COCO 中包含了更小的物体(面积不足整张图片的 1%),而且物体的分布更加稠密,这些特性使得 MS-COCO 数据集更加接近真实...
This work focuses on the aspect of facial manipulation in Deepfake, encompassing Face Swapping, Face Reenactment, Talking Face Generation, Face Attribute Editing and Forgery Detection. We believe this will be the most comprehensive survey to date on facial manipulation and detection technologies. Please...
A Survey of Deep Learning-Based Object Detection Recent Advances in Deep Learning for Object Detection Recurrent Scale Approximation for Object Detection in CNN Relation Networks for Object Detection Single-Shot Refinement Neural Network for Object Detection ...
[192]提出了3D fully-convolutional Semantic Instance Segmentation (3D-SIS) network,来实现在RGB-D数据上的语义实例分割。该网络从颜色和几何中学习特征。与3D目标检测类似,3D Region Proposal Network(3D-RPN)和3D ROI layer用来预测bounding box的位置,物体类别和实例的mask。根据合成分析策略,[193]提出了...