Fetching the paper…
Reading the bibliography…
We present a simple, fully-convolutional model for real-time (>30 fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach.
1901
Earlier work this paper cites.
T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons,” IJCV , 2001
2001
Earlier work this paper cites.
2002
Earlier work this paper cites.
S. Agarwal and D. Roth, “Learning a sparse representation for object detection,” in ECCV , 2002
2002
Earlier work this paper cites.
J. Sivic and A. Zisserman, “Video google: A text retrieval approach to object matching in videos,” in ICCV , 2003
2003
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A Large-Scale Hierarchical Image Database,” in CVPR , 2009
2009
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International Journal of Computer Vision , vol. 88, no. 2, pp. 303–338, Jun. 2010
2010
Earlier work this paper cites.
J. Yang, J. Wright, T. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Transactions on Image Processing , 2010
2010
Earlier work this paper cites.
J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in CVPR , 2010
2010
Earlier work this paper cites.
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik, “Semantic contours from inverse detectors,” in ICCV , 2011
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in CVPR , 2012
2012
Earlier work this paper cites.
T. Zhang, B. Ghanem, S. Liu, C. Xu, and N. Ahuja, “Low-rank sparse coding for image classification,” in ICCV , 2013
2013
Earlier work this paper cites.
X. Ren and D. Ramanan, “Histograms of sparse codes for object detection,” in CVPR , 2013
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in NeurIPS , 2015
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015
2015
Earlier work this paper cites.
J. Dai, Y. Li, K. He, and J. Sun, “R-fcn: Object detection via region-based fully convolutional networks,” in NeurIPS , 2016
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. Berg, “Ssd: Single shot multibox detector,” in ECCV , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Cited alongside, same era.
J. Dai, K. He, Y. Li, S. Ren, and J. Sun, “Instance-sensitive fully convolutional networks,” in ECCV , 2016
2016
Cited alongside, same era.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in CVPR , 2016
2016
Cited alongside, same era.
M. Treml, J. Arjona-Medina, T. Unterthiner, R. Durgesh, F. Friedmann, P. Schuberth, A. Mayr, M. Heusel, M. Hofmarcher, M. Widrich et al. , “Speeding up semantic segmentation for autonomous driving,” in MLITS, NIPS Workshop , vol. 2, no. 7, 2016
2016
Cited alongside, same era.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, “Focal Loss for Dense Object Detection,” 2017, pp. 2980–2988. [Online]. Available: https://openaccess.thecvf.com/content_iccv_2017/html/Lin_Focal_Loss_for_ICCV_2017_paper.html
2017
Later among the works it cites.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 39, no. 12, pp. 2481–2495, Dec. 2017, conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence
2017
Later among the works it cites.
N. Dvornik, K. Shmelkov, J. Mairal, and C. Schmid, “Blitznet: A real-time deep network for scene understanding,” in ICCV , 2017
2017
Later among the works it cites.
S. Jetley, M. Sapienza, S. Golodetz, and P. Torr, “Straight to shapes: real-time detection of encoded shapes,” in CVPR , 2017
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in CVPR , 2016
2016
Cited alongside, same era.
A. Shrivastava, A. Gupta, and R. Girshick, “Training region-based object detectors with online hard example mining,” in CVPR , 2016
2016
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in ICCV , 2017
2017
Cited alongside, same era.
Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei, “Fully convolutional instance-aware semantic segmentation,” in CVPR , 2017
2017
Cited alongside, same era.
J. Redmon and A. Farhadi, “Yolo9000: Better, faster, stronger,” in CVPR , 2017
2017
Cited alongside, same era.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 764–773
2017
Cited alongside, same era.
A. Kirillov, E. Levinkov, B. Andres, B. Savchynskyy, and C. Rother, “Instancecut: from edges to instances with multicut,” in CVPR , 2017
2017
Cited alongside, same era.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in CVPR , 2017
2017
Later among the works it cites.
2018
Later among the works it cites.
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in CVPR , 2018
2018
Later among the works it cites.
L.-C. Chen, A. Hermans, G. Papandreou, F. Schroff, P. Wang, and H. Adam, “Masklab: Instance segmentation by refining object detection with semantic and direction features,” in CVPR , 2018
2018
Later among the works it cites.
X. Liang, L. Lin, Y. Wei, X. Shen, J. Yang, and S. Yan, “Proposal-free network for instance-level object segmentation,” TPAMI , 2018
2018
Later among the works it cites.
H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia, “Icnet for real-time semantic segmentation on high-resolution images,” in ECCV , 2018
2018
Later among the works it cites.
J. Uhrig, E. Rehder, B. Fröhlich, U. Franke, and T. Brox, “Box2pix: Single-shot instance segmentation by assigning pixels to object boxes,” in IEEE Intelligent Vehicles Symposium , 2018
2018
Later among the works it cites.
D. Bolya, C. Zhou, F. Xiao, and Y. J. Lee, “Yolact: Real-time instance segmentation,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
Closest in time.
X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 9308–9316
2019
Closest in time.
Z. Huang, L. Huang, Y. Gong, C. Huang, and X. Wang, “Mask scoring r-cnn,” in CVPR , 2019
2019
Closest in time.
D. Neven, B. D. Brabandere, M. Proesmans, and L. V. Gool, “Instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8837–8845
2019
Closest in time.
A. Araujo, W. Norris, and J. Sim, “Computing receptive fields of convolutional neural networks,” Distill , 2019
2019
Closest in time.
J. Dai, K. He, and J. Sun, “Instance-aware semantic segmentation via multi-task network cascades,” in CVPR , 2016
2019
Closest in time.
E. Xie, P. Sun, X. Song, W. Wang, X. Liu, D. Liang, C. Shen, and P. Luo, “PolarMask: Single Shot Instance Segmentation With Polar Representation,” 2020, pp. 12 193–12 202. [Online]. Available: https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_PolarMask_Single_Shot_Instance_Segmentation_With_Polar_Representation_CVPR_2020_paper.html
2020
Closest in time.