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Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

Nizar Massouh
F. Babiloni
Tatiana Tommasi
Jay Young
Nick Hawes
Barbara Caputo
    VLM
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Papers citing "Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work"

4 / 4 papers shown
Title
A deep representation for depth images from synthetic data
A deep representation for depth images from synthetic data
Fabio Maria Carlucci
P. Russo
Barbara Caputo
MDE
3DV
25
34
0
30 Sep 2016
The STRANDS Project: Long-Term Autonomy in Everyday Environments
The STRANDS Project: Long-Term Autonomy in Everyday Environments
Nick Hawes
Christopher Burbridge
Ferdian Jovan
Lars Kunze
Bruno Lacerda
...
Jaime Pulido Fentanes
T. Krajník
J. M. Santos
T. Duckett
Marc Hanheide
45
216
0
15 Apr 2016
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Saurabh Gupta
Ross B. Girshick
Pablo Arbeláez
Jitendra Malik
ObjD
89
1,560
0
22 Jul 2014
Caffe: Convolutional Architecture for Fast Feature Embedding
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia
Evan Shelhamer
Jeff Donahue
Sergey Karayev
Jonathan Long
Ross B. Girshick
S. Guadarrama
Trevor Darrell
VLM
BDL
3DV
183
14,703
0
20 Jun 2014