whz1861的博客

This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer.模型 说明：基础模型采用VGG16 去掉fc层，使得encoder网络更小，更易训练134M –

Repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. We present RefineNet, a generic multi-path refinemen

In this paper, we exploit the capability of global context information by different-region-based context aggregation through out pyramid pooling module together with the proposed pyramid scene parsing

In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model is trained jointly with two objectives: given an

We present the first fully convolutional end-to-end solution for instane-aware semantic segmentation task. It detects and segments the object instances jointly and simultanously. By the introduction

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality s

We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-art in semantic segmentation We define a novel architecture that combines semantic inform