Search results with tag "Semantic segmentation"
Rethinking BiSeNet for Real-Time Semantic Segmentation
openaccess.thecvf.comgroup convolution to reduce computation cost while main-taining comparable accuracy. These works are particularly designed for the image classification tasks, and their exten-sions to semantic segmentation application should be care-fully tuned. 2.2. Generic Semantic Segmentation Traditional segmentation algorithms, e.g., threshold se-
![Abstract arXiv:2107.06278v2 [cs.CV] 31 Oct 2021](/cache/preview/d/0/3/c/1/4/1/b/thumb-d03c141b31b29476ec074e3c5409c25c.jpg)
Abstract arXiv:2107.06278v2 [cs.CV] 31 Oct 2021
arxiv.orgThe goal of semantic segmentation is to partition an image into regions with different semantic categories. Starting from Fully Convolutional Networks (FCNs) work of Long et al. [30], most deep learning-based semantic segmentation approaches formulate semantic segmentation as …
Three Ways To Improve Semantic Segmentation With Self ...
openaccess.thecvf.comSDE and semantic segmentation and show that combining SDE with ImageNet features can even further boost perfor-mance. Novosel et al. [42] and Klingner et al. [29] improve the semantic segmentation performance by jointly learning SDE. However, they focus on the fully-supervised setting, while our work explicitly addresses the challenges of semi-
Semi-Supervised Semantic Segmentation With Directional ...
openaccess.thecvf.comSegmentation networks can not predict a label for each pixel merely based on its RGB values. Therefore, the con-textual information is essential for semantic segmentation. Iconic models (e.g., DeepLab [7] and PSPNet [60]) have also shown satisfying performance by adequately aggregat-ing the contextual cues to individual pixels before making
Convolutional Neural Network - 國立臺灣大學
speech.ee.ntu.edu.twFully Connected Feedforward network output. ... object detection and semantic segmentation”, CVPR, 2014. Convolution Max Pooling Convolution Max Pooling input 25 3x3 filters 50 3x3 ... “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps”, ICLR, 2014 | ...
Spatial Transformer Networks - NeurIPS
proceedings.neurips.ccConvolutional Neural Networks define an exceptionally powerful class of models, ... localisation, semantic segmentation, and action recognition tasks, amongst others. ... can take any form, such as a fully-connected network or a convolutional network, but should include a final regression layer to produce the transformation ...
Zhi Tian Chunhua Shen Hao Chen Tong He The University of ...
arxiv.orgRecently, fully convolutional networks (FCNs) [20] have achieved tremendous success in dense prediction tasks such as semantic segmentation [20, 28, 9, 19], depth estimation arXiv:1904.01355v5 [cs.CV] 20 Aug 2019
![arXiv:1812.01187v2 [cs.CV] 5 Dec 2018](/cache/preview/c/b/1/2/d/d/5/b/thumb-cb12dd5b147004c786bfda6881481412.jpg)
arXiv:1812.01187v2 [cs.CV] 5 Dec 2018
arxiv.orgplication domains such as object detection and semantic segmentation. 1. Introduction Since the introduction of AlexNet [15] in 2012, deep convolutional neural networks have become the dominat-ing approach for image classification. Various new architec-tures have been proposed since then, including VGG [24],
![Abstract arXiv:1411.4038v2 [cs.CV] 8 Mar 2015](/cache/preview/e/8/a/1/0/5/9/a/thumb-e8a1059a2748cde7ba3d756f44e4ca34.jpg)
Abstract arXiv:1411.4038v2 [cs.CV] 8 Mar 2015
arxiv.orgFully Convolutional Networks for Semantic Segmentation Jonathan Long Evan Shelhamer Trevor Darrell UC Berkeley fjonlong,shelhamer,trevorg@cs.berkeley.edu