ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1710.05126
  4. Cited By
Hierarchical semantic segmentation using modular convolutional neural
  networks

Hierarchical semantic segmentation using modular convolutional neural networks

14 October 2017
S. Eppel
ArXiv (abs)PDFHTML

Papers citing "Hierarchical semantic segmentation using modular convolutional neural networks"

25 / 25 papers shown
Title
Setting an attention region for convolutional neural networks using
  region selective features, for recognition of materials within glass vessels
Setting an attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels
S. Eppel
29
25
0
29 Aug 2017
A Review on Deep Learning Techniques Applied to Semantic Segmentation
A Review on Deep Learning Techniques Applied to Semantic Segmentation
Alberto Garcia-Garcia
Sergio Orts
Sergiu Oprea
Victor Villena-Martinez
Jose Garcia-Rodriguez
3DVSSeg
112
1,278
0
22 Apr 2017
Objects as context for detecting their semantic parts
Objects as context for detecting their semantic parts
Abel Gonzalez-Garcia
Davide Modolo
V. Ferrari
49
6
0
28 Mar 2017
Look into Person: Self-supervised Structure-sensitive Learning and A New
  Benchmark for Human Parsing
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Ke Gong
Xiaodan Liang
Dongyu Zhang
Xiaohui Shen
Liang Lin
SSL
48
476
0
16 Mar 2017
Modular Representation of Layered Neural Networks
Modular Representation of Layered Neural Networks
C. Watanabe
Kaoru Hiramatsu
K. Kashino
AI4TS
64
56
0
01 Mar 2017
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
Chrisantha Fernando
Dylan Banarse
Charles Blundell
Yori Zwols
David R Ha
Andrei A. Rusu
Alexander Pritzel
Daan Wierstra
75
880
0
30 Jan 2017
Outrageously Large Neural Networks: The Sparsely-Gated
  Mixture-of-Experts Layer
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam M. Shazeer
Azalia Mirhoseini
Krzysztof Maziarz
Andy Davis
Quoc V. Le
Geoffrey E. Hinton
J. Dean
MoE
248
2,653
0
23 Jan 2017
COCO-Stuff: Thing and Stuff Classes in Context
COCO-Stuff: Thing and Stuff Classes in Context
Holger Caesar
J. Uijlings
V. Ferrari
132
1,387
0
12 Dec 2016
SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks
  for Image Captioning
SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning
Long Chen
Hanwang Zhang
Jun Xiao
Liqiang Nie
Jian Shao
Wei Liu
Tat-Seng Chua
68
1,662
0
17 Nov 2016
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised
  Localization
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
Vadim Kantorov
Maxime Oquab
Minsu Cho
Ivan Laptev
WSOL
62
306
0
14 Sep 2016
Semantic Understanding of Scenes through the ADE20K Dataset
Semantic Understanding of Scenes through the ADE20K Dataset
Bolei Zhou
Hang Zhao
Xavier Puig
Tete Xiao
Sanja Fidler
Adela Barriuso
Antonio Torralba
SSeg
402
1,881
0
18 Aug 2016
Do semantic parts emerge in Convolutional Neural Networks?
Do semantic parts emerge in Convolutional Neural Networks?
Abel Gonzalez-Garcia
Davide Modolo
V. Ferrari
174
113
0
13 Jul 2016
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
  Atrous Convolution, and Fully Connected CRFs
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen
George Papandreou
Iasonas Kokkinos
Kevin Patrick Murphy
Alan Yuille
SSeg
251
18,240
0
02 Jun 2016
Fully Convolutional Networks for Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation
Evan Shelhamer
Jonathan Long
Trevor Darrell
VOSSSeg
741
37,862
0
20 May 2016
A guide to convolution arithmetic for deep learning
A guide to convolution arithmetic for deep learning
Vincent Dumoulin
Francesco Visin
FAtt3DHHAI
66
1,542
0
23 Mar 2016
Tracing liquid level and material boundaries in transparent vessels
  using the graph cut computer vision approach
Tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach
S. Eppel
28
14
0
31 Jan 2016
Where To Look: Focus Regions for Visual Question Answering
Where To Look: Focus Regions for Visual Question Answering
Kevin J. Shih
Saurabh Singh
Derek Hoiem
73
460
0
23 Nov 2015
ABC-CNN: An Attention Based Convolutional Neural Network for Visual
  Question Answering
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
Kan Chen
Jiang Wang
Liang-Chieh Chen
Haoyuan Gao
Wenyuan Xu
Ram Nevatia
73
288
0
18 Nov 2015
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image
  Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan
Alex Kendall
R. Cipolla
SSeg
1.1K
15,802
0
02 Nov 2015
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal
  Networks
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren
Kaiming He
Ross B. Girshick
Jian Sun
AIMatObjD
520
62,294
0
04 Jun 2015
Deep Learning for Semantic Part Segmentation with High-Level Guidance
Deep Learning for Semantic Part Segmentation with High-Level Guidance
Stavros Tsogkas
Iasonas Kokkinos
George Papandreou
A. Vedaldi
SSeg
51
32
0
10 May 2015
Tracing the boundaries of materials in transparent vessels using
  computer vision
Tracing the boundaries of materials in transparent vessels using computer vision
S. Eppel
24
5
0
20 Jan 2015
Detect What You Can: Detecting and Representing Objects using Holistic
  Models and Body Parts
Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts
Xianjie Chen
Roozbeh Mottaghi
Xiaobai Liu
Sanja Fidler
R. Urtasun
Alan Yuille
98
640
0
08 Jun 2014
Microsoft COCO: Common Objects in Context
Microsoft COCO: Common Objects in Context
Nayeon Lee
Michael Maire
Serge J. Belongie
Lubomir Bourdev
Ross B. Girshick
James Hays
Pietro Perona
Deva Ramanan
C. L. Zitnick
Piotr Dollár
ObjD
413
43,667
0
01 May 2014
Computer vision-based recognition of liquid surfaces and phase
  boundaries in transparent vessels, with emphasis on chemistry applications
Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications
S. Eppel
Tal Kachman
53
26
0
28 Apr 2014
1