Fetching the paper…
Reading the bibliography…
We propose an approach to semantic segmentation that achieves state-of-the-art supervised performance when applied in a zero-shot setting.
1905
Earlier work this paper cites.
1908
Earlier work this paper cites.
Z. Gu, S. Zhou, L. Niu, Z. Zhao, and L. Zhang, “Context-aware feature generation for zero-shot semantic segmentation,” in ACM Int. Conf. Multimedia , 2020, pp. 1921–1929
1929
Earlier work this paper cites.
G. A. Miller, “Wordnet: a lexical database for english,” Communications of the ACM , vol. 38, no. 11, pp. 39–41, 1995
1995
Earlier work this paper cites.
G. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla, “Segmentation and recognition using structure from motion point clouds,” in Eur. Conf. Comput. Vis. Springer, 2008, pp. 44–57
2008
Earlier work this paper cites.
H. Zhao, X. Puig, B. Zhou, S. Fidler, and A. Torralba, “Open vocabulary scene parsing,” in Int. Conf. Comput. Vis. , 2017, pp. 2002–2010
2010
Earlier work this paper cites.
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from rgbd images,” in Eur. Conf. Comput. Vis. Springer, 2012, pp. 746–760
2012
Earlier work this paper cites.
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black, “A naturalistic open source movie for optical flow evaluation,” in Eur. Conf. Comput. Vis. , 2012, pp. 611–625
2012
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Adv. Neural Inform. Process. Syst. , 2013, pp. 3111–3119
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” Int. J. of Rob. Research , 2013
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Eur. Conf. Comput. Vis. Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille, “The role of context for object detection and semantic segmentation in the wild,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2014, pp. 891–898
2014
Earlier work this paper cites.
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” Int. J. Comput. Vis. , vol. 111, no. 1, pp. 98–136, 2015
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
S. Song, S. P. Lichtenberg, and J. Xiao, “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2015, pp. 567–576
2015
Earlier work this paper cites.
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 IEEE Conf. Comput. Vis. Pattern Recog. , 2016, pp. 3213–3223
2016
Earlier work this paper cites.
G. Ros, S. Stent, P. F. Alcantarilla, and T. Watanabe, “Training constrained deconvolutional networks for road scene semantic segmentation,” arXiv: Comp. Res. Repository , p. 1604.01545, 2016
2016
Earlier work this paper cites.
W. Chen, Z. Fu, D. Yang, and J. Deng, “Single-image depth perception in the wild,” in Adv. Neural Inform. Process. Syst. , 2016, pp. 730–738
2016
Earlier work this paper cites.
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba, “Scene parsing through ade20k dataset,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2017
2017
Earlier work this paper cites.
G. Neuhold, T. Ollmann, S. Rota Bulo, and P. Kontschieder, “The mapillary vistas dataset for semantic understanding of street scenes,” in Int. Conf. Comput. Vis. , 2017, pp. 4990–4999
2017
Earlier work this paper cites.
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2017, pp. 5828–5839
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Int. Conf. Comput. Vis. , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
O. Zendel, K. Honauer, M. Murschitz, D. Steininger, and G. F. Dominguez, “Wilddash-creating hazard-aware benchmarks,” in Eur. Conf. Comput. Vis. Springer, 2018, pp. 402–416
2018
Earlier work this paper cites.
K. Xian, C. Shen, Z. Cao, H. Lu, Y. Xiao, R. Li, and Z. Luo, “Monocular relative depth perception with web stereo data supervision,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018, pp. 311–320
2018
Earlier work this paper cites.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Eur. Conf. Comput. Vis. Springer, 2018, pp. 801–818
2018
Earlier work this paper cites.
A. Zamir, A. Sax, , W. Shen, L. Guibas, J. Malik, and S. Savarese, “Taskonomy: Disentangling task transfer learning,” in IEEE Conf. Comput. Vis. Pattern Recog. IEEE, 2018
2018
Cited alongside, same era.
Y. Kim, H. Jung, D. Min, and K. Sohn, “Deep monocular depth estimation via integration of global and local predictions,” IEEE Trans. Image Process. , vol. 27, no. 8, pp. 4131–4144, 2018
2018
Cited alongside, same era.
Y. Xian, S. Choudhury, Y. He, B. Schiele, and Z. Akata, “Semantic projection network for zero-and few-label semantic segmentation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2019, pp. 8256–8265
2019
Cited alongside, same era.
R. Benenson, S. Popov, and V. Ferrari, “Large-scale interactive object segmentation with human annotators,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2019
2019
Cited alongside, same era.
K. Xian, J. Zhang, O. Wang, L. Mai, Z. Lin, and Z. Cao, “Structure-guided ranking loss for single image depth prediction,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020, pp. 611–620
2020
Later among the works it cites.
H. Chen, K. Sun, Z. Tian, C. Shen, Y. Huang, and Y. Yan, “BlendMask: Top-down meets bottom-up for instance segmentation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020
2020
Later among the works it cites.
Z. Tian, C. Shen, and H. Chen, “Conditional convolutions for instance segmentation,” in Eur. Conf. Comput. Vis. Springer, 2020
2020
Later among the works it cites.
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,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020, pp. 12 193–12 202
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Shao, Z. Li, T. Zhang, C. Peng, G. Yu, X. Zhang, J. Li, and J. Sun, “Objects365: A large-scale, high-quality dataset for object detection,” in Int. Conf. Comput. Vis. , 2019, pp. 8430–8439
2019
Cited alongside, same era.
M. Bucher, T.-H. Vu, M. Cord, and P. Pérez, “Zero-shot semantic segmentation,” Adv. Neural Inform. Process. Syst. , vol. 32, pp. 468–479, 2019
2019
Cited alongside, same era.
P. Bevandić, I. Krešo, M. Oršić, and S. Šegvić, “Simultaneous semantic segmentation and outlier detection in presence of domain shift,” in German Conf. Pattern Recogn. Springer, 2019, pp. 33–47
2019
Cited alongside, same era.
G. Varma, A. Subramanian, A. Namboodiri, M. Chandraker, and C. Jawahar, “Idd: A dataset for exploring problems of autonomous navigation in unconstrained environments,” in IEEE Winter Conf. on Applic. of Comp. Vis. IEEE, 2019, pp. 1743–1751
2019
Cited alongside, same era.
L. Yang, Y. Fan, and N. Xu, “Video instance segmentation,” in Int. Conf. Comput. Vis. , 2019, pp. 5188–5197
2019
Cited alongside, same era.
Y. Zhu, K. Sapra, F. A. Reda, K. J. Shih, S. Newsam, A. Tao, and B. Catanzaro, “Improving semantic segmentation via video propagation and label relaxation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2019, pp. 8856–8865
2019
Cited alongside, same era.
C. Liu, L.-C. Chen, F. Schroff, H. Adam, W. Hua, A. L. Yuille, and L. Fei-Fei, “Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2019, pp. 82–92
2019
Cited alongside, same era.
K. Sun, B. Xiao, D. Liu, and J. Wang, “Deep high-resolution representation learning for human pose estimation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
D. Baek, Y. Oh, and B. Ham, “Exploiting a joint embedding space for generalized zero-shot semantic segmentation,” in Int. Conf. Comput. Vis. , 2021, pp. 9536–9545
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” arXiv: Comp. Res. Repository , p. 2103.00020, 2021
2021
Later among the works it cites.
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “SegFormer: Simple and efficient design for semantic segmentation with transformers,” Adv. Neural Inform. Process. Syst. , 2021
2021
Later among the works it cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” Int. Conf. Comput. Vis. , 2021
2021
Later among the works it cites.
B. Cheng, A. G. Schwing, and A. Kirillov, “Per-pixel classification is not all you need for semantic segmentation,” 2021
2021
Later among the works it cites.
W. Yin, J. Zhang, O. Wang, S. Niklaus, L. Mai, S. Chen, and C. Shen, “Learning to recover 3d scene shape from a single image,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2021
2021
Later among the works it cites.
S. Bujwid and J. Sullivan, “Large-scale zero-shot image classification from rich and diverse textual descriptions,” arXiv: Comp. Res. Repository , 2021
2021
Later among the works it cites.
W. Yin, Y. Liu, and C. Shen, “Virtual normal: Enforcing geometric constraints for accurate and robust depth prediction,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2021
2021
Later among the works it cites.
Y. Huang, W. Jia, X. He, L. Liu, Y. Li, and D. Tao, “Channelized axial attention for semantic segmentation,” arXiv: Comp. Res. Repository , p. 2101.07434, 2021
2021
Later among the works it cites.
J. Cao, H. Leng, D. Lischinski, D. Cohen-Or, C. Tu, and Y. Li, “Shapeconv: Shape-aware convolutional layer for indoor rgb-d semantic segmentation,” arXiv: Comp. Res. Repository , p. 2108.10528, 2021
2021
Later among the works it cites.
R. Ranftl, A. Bochkovskiy, and V. Koltun, “Vision transformers for dense prediction,” in Int. Conf. Comput. Vis. , 2021, pp. 12 179–12 188
2021
Later among the works it cites.
B. Li, K. Q. Weinberger, S. Belongie, V. Koltun, and R. Ranftl, “Language-driven semantic segmentation,” in Int. Conf. Learn. Represent. , 2022
2022
Closest in time.
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” 2022
2022
Closest in time.
J. Ding, N. Xue, G.-S. Xia, and D. Dai, “Decoupling zero-shot semantic segmentation,” 2022
2022
Closest in time.
W. Yin, J. Zhang, O. Wang, S. Niklaus, S. Chen, Y. Liu, and C. Shen, “Towards accurate reconstruction of 3d scene shape from a single monocular image,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
2022
Closest in time.
W. Yin, C. Zhang, H. Chen, Z. Cai, G. Yu, K. Wang, X. Chen, and C. Shen, “Metric3d: Towards zero-shot metric 3d prediction from a single image,” Int. Conf. Comput. Vis. , 2023
2023
Closest in time.
Z. Li and N. Snavely, “Megadepth: Learning single-view depth prediction from internet photos,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018, pp. 2041–2050
2050
Closest in time.
X. Chen, R. Girshick, K. He, and P. Dollár, “Tensormask: A foundation for dense object segmentation,” in Int. Conf. Comput. Vis. , 2019, pp. 2061–2069
2069
Closest in time.