Parallel Feature Pyramid Network for Object Detection ECCV2018 总结: 文章借鉴了SPP的思想并通过MSCA(multi-scale context aggregation)模块进行特征融合从而提出PFPNet(Parallel Feature Pyramid Network)算法来提升目标检测的效果。 1.使用spp模块通过扩大网络宽度而不是增加深度来生成金字塔形特征图 2.提出msca模块,有效...
Recently developed object detectors employ a convolutional neural network (CNN) by gradually increasing the number of feature layers with a pyramidal shape instead of using a featurized image pyramid. However, the different abstraction levels of CNN feature layers often limit the detection performance, ...
This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting...
We first design a feature pyramid network with two parallel branches (PB-FPN), corresponding to the top-down and bottom-up feature fusion pathways, respectively. Different optimization modules can be adopted in different pathways to avoid potential module incompatibility when connected in series. Such...
A pyramid network of sizepis a complete 4-ary rooted tree of height log4paugmented with additional interprocessor links so that the processors in every tree level form a two-dimensional grid network. A pyramid of sizephas at its base a two-dimensional grid network containingp=k2processors where...
This feature sharing approach is also used for multiclass object detection. To accelerate the parallel structure, Li and Zhang (2004) propose a pyramid structure in a coarse-to-fine fashion shown in Figure 2.2 for multipose detection. It consists of several levels from the coarsest view ...
network in parallel, which model the long-dependence and local information of the feature map, respectively. The outputs are fused through a GLF attention module for subsequent processing. A standard transformer is added between the encoder and the decoder to integrate features after dimension ...
where IrealL and IrealT are the i-th LR and GT image pair, and F (IrealL; θ) is the network ψ(·) output for IrealL with parameters θ, IrealS is the super-resolution image. All parameters are optimized using optimization function. More details are illustrated in Appendix 1. The ...
Literature27 proposes a hybrid neural network model to convert PQD into a perturbed image, using CNN to automatically extract spatial features of the image, and then inputting the temporal features into a gated recurrent unit (GRU) for classification. This method extracts the spatial-temporal ...
Shuffle-fusion pyramid network for bearing fault diagnosis under noisy environments Recent advancements in deep learning have driven the development of big data-driven fault diagnosis techniques. However, traditional models often face sign... C Zhao,L Deng,Y Zhang,... - IOP Publishing Ltd 被引量...