Fetching the paper…
Reading the bibliography…
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
The need for biases in learning generalizations
T. M. Mitchell · 1980
Earlier work this paper cites.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
Regularized multi–task learning
T. Evgeniou and M. Pontil · 2004
Earlier work this paper cites.
Real time detection of lane markers in urban streets
M. Aly · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Pedestrian detection: A benchmark
P. Dollár, C. Wojek, B. Schiele, and P. Perona · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. R. Zamir, and M. Shah · 2012
Earlier work this paper cites.
A practical system for road marking detection and recognition
T. Wu and A. Ranganathan · 2012
Earlier work this paper cites.
Structured forests for fast edge detection
P. Dollár and C. L. Zitnick · 2013
Earlier work this paper cites.
A new performance measure and evaluation benchmark for road detection algorithms
J. Fritsch, T. Kuhnl, and A. Geiger · 2013
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Earlier work this paper cites.
Activitynet: A large-scale video benchmark for human activity understanding
F. Caba Heilbron, V. Escorcia, B. Ghanem, and J. Carlos Niebles · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
Cited alongside, same era.
Youtube-8m: A large-scale video classification benchmark
S. Abu-El-Haija, N. Kothari, J. Lee, P. Natsev, G. Toderici, B. Varadarajan, and S. Vijayanarasimhan · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Cited alongside, same era.
Learning multiple visual domains with residual adapters
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2017
Later among the works it cites.
Learning multiple visual domains with residual adapters
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2017
Later among the works it cites.
End-to-end learning of driving models from large-scale video datasets
H. Xu, Y. Gao, F. Yu, and T. Darrell · 2017
Later among the works it cites.
Dilated residual networks
F. Yu, V. Koltun, and T. Funkhouser · 2017
Later among the works it cites.
Citypersons: A diverse dataset for pedestrian detection
S. Zhang, R. Benenson, and B. Schiele · 2017
Later among the works it cites.
Efficient interactive annotation of segmentation datasets with polygon-rnn++
D. Acuna, H. Ling, A. Kar, and S. Fidler · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Mot16: A benchmark for multi-object tracking
A. Milan, L. Leal-Taixé, I. Reid, S. Roth, and K. Schindler · 2016
Cited alongside, same era.
Detect to track and track to detect
C. Feichtenhofer, A. Pinz, and A. Zisserman · 2017
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
VPGNet: Vanishing point guided network for lane and road marking detection and recognition
S. Lee, J. Kim, J. S. Yoon, S. Shin, O. Bailo, N. Kim, T.-H. Lee, H. S. Hong, S.-H. Han, and I. S. Kweon · 2017
Cited alongside, same era.
1 year, 1000 km: The oxford robotcar dataset
W. Maddern, G. Pascoe, C. Linegar, and P. Newman · 2017
Cited alongside, same era.
The mapillary vistas dataset for semantic understanding of street scenes
G. Neuhold, T. Ollmann, S. R. Bulò, and P. Kontschieder · 2017
Cited alongside, same era.
B. McCann, N. S. Keskar, C. Xiong, and R. Socher · 2018
Closest in time.
Youtube-vos: A large-scale video object segmentation benchmark
N. Xu, L. Yang, Y. Fan, D. Yue, Y. Liang, J. Yang, and T. Huang · 2018
Closest in time.
Deep layer aggregation
F. Yu, D. Wang, E. Shelhamer, and T. Darrell · 2018
Closest in time.
Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. Shen, L. J. Guibas, J. Malik, and S. Savarese · 2018
Closest in time.
Scalability in perception for autonomous driving: Waymo open dataset
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, et al · 2019
Closest in time.
Mots: Multi-object tracking and segmentation
P. Voigtlaender, M. Krause, A. Osep, J. Luiten, B. B. G. Sekar, A. Geiger, and B. Leibe · 2019
Closest in time.
Learning saliency propagation for semi-supervised instance segmentation
Y. Zhou, X. Wang, J. Jiao, T. Darrell, and F. Yu · 2020
Closest in time.