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4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
13 June 2024
Roman Bachmann
Oğuzhan Fatih Kar
David Mizrahi
Ali Garjani
Mingfei Gao
David Griffiths
Jiaming Hu
Afshin Dehghan
Amir Zamir
MoE
VLM
MLLM
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Papers citing
"4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities"
7 / 57 papers shown
Title
An Overview of Multi-Task Learning in Deep Neural Networks
Sebastian Ruder
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2,831
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal
Piotr Dollár
Ross B. Girshick
P. Noordhuis
Lukasz Wesolowski
Aapo Kyrola
Andrew Tulloch
Yangqing Jia
Kaiming He
3DH
128
3,685
0
08 Jun 2017
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Alex Kendall
Y. Gal
R. Cipolla
3DH
272
3,136
0
19 May 2017
Deep Networks with Stochastic Depth
Gao Huang
Yu Sun
Zhuang Liu
Daniel Sedra
Kilian Q. Weinberger
215
2,362
0
30 Mar 2016
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
David Eigen
Rob Fergus
VLM
MDE
209
2,683
0
18 Nov 2014
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLM
ObjD
1.7K
39,615
0
01 Sep 2014
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
434
43,832
0
01 May 2014
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