![](bg8.png)
REFERENCES
Arnab, A. and Torr, P. (2017). Pixelwise Instance Segmen-
tation with a Dynamically Instantiated Network. In
Conference on Computer Vision and Pattern Recogni-
tion.
Bandera, C. and Scott, P. D. (1989). Foveal machine vi-
sion systems. In Systems, Man and Cybernetics, 1989.
Conference Proceedings., IEEE International Confe-
rence on, pages 596–599. IEEE.
Chen, L.-C., Fidler, S., Yuille, A. L., and Urtasun, R.
(2014). Beat the mturkers: Automatic image labeling
from weak 3d supervision. In Conference on Compu-
ter Vision and Pattern Recognition, pages 3198–3205.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler,
M., Benenson, R., Franke, U., Roth, S., and Schiele,
B. (2016). The Cityscapes Dataset for Semantic Urban
Scene Understanding. In Conference on Computer Vi-
sion and Pattern Recognition.
Dai, J., He, K., and Sun, J. (2016). Instance-aware Seman-
tic Segmentation via Multi-task Network Cascades. In
Conference on Computer Vision and Pattern Recogni-
tion.
Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we re-
ady for autonomous driving? The KITTI vision ben-
chmark suite. In Conference on Computer Vision and
Pattern Recognition.
Girshick, R. (2015). Fast R-CNN. In International Confe-
rence on Computer Vision.
Hayder, Z., He, X., and Salzmann, M. (2016). Shape-
aware Instance Segmentation. arXiv preprint
arXiv:1612.03129.
He, K., Gkioxari, G., Doll
´
ar, P., and Girshick, R. (2017).
Mask R-CNN. arXiv preprint arXiv:1703.06870.
Hu, P. and Ramanan, D. (2016). Finding tiny faces. arXiv
preprint arXiv:1612.04402.
Kirillov, A., Levinkov, E., Andres, B., Savchynskyy, B., and
Rother, C. (2016). InstanceCut: from Edges to Instan-
ces with MultiCut. arXiv preprint arXiv:1611.08272.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ra-
manan, D., Doll
´
ar, P., and Zitnick, C. L. (2014). Mi-
crosoft COCO: Common objects in context. In Euro-
pean Conference on Computer Vision, pages 740–755.
Springer.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu,
C.-Y., and Berg, A. C. (2016). SSD: Single shot mul-
tibox detector. In European Conference on Computer
Vision, pages 21–37. Springer.
Long, J., Shelhamer, E., and Darrell, T. (2015). Fully
Convolutional Models for Semantic Segmentation. In
Conference on Computer Vision and Pattern Recogni-
tion.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-
CNN: Towards real-time object detection with region
proposal networks. In Advances in Neural Informa-
tion Processing Systems, pages 91–99.
Romera-Paredes, B. and Torr, P. H. S. (2016). Recurrent
instance segmentation. In arXiv:1511.08250.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net:
Convolutional networks for biomedical image seg-
mentation. In MICCAI.
Schwartz, E. L. (1977). Spatial mapping in the primate
sensory projection: analytic structure and relevance to
perception. Biological cybernetics, 25(4):181–194.
Simonyan, K. and Zisserman, A. (2015). Very deep con-
volutional networks for large-scale image recognition.
In International Conference on Learning Representa-
tions.
Ude, A., Atkeson, C. G., and Cheng, G. (2003). Com-
bining peripheral and foveal humanoid vision to de-
tect, pursue, recognize and act. In Intelligent Robots
and Systems, 2003.(IROS 2003). Proceedings. 2003
IEEE/RSJ International Conference on, volume 3, pa-
ges 2173–2178. IEEE.
Uhrig, J., Cordts, M., Franke, U., and Brox, T. (2016).
Pixel-level encoding and depth layering for instance-
level semantic segmentation. In German Conference
on Pattern Recognition.
van den Brand, J., Ochs, M., and Mester, R. (2016).
Instance-level Segmentation of Vehicles using Deep
Contours. In Asian Conference on Computer Vision.
Viola, P. and Jones, M. J. (2004). Robust real-time face
detection. International Journal of Computer Vision,
57(2):137–154.
Wang, Z. and Bovik, A. C. (2006). Foveated image and
video coding. Digital Video, Image Quality and Per-
ceptual Coding, pages 431–457.
Zhang, Z., A., S., S., F., and R., U. (2015). Monocular
object instance segmentation and depth ordering with
CNNs. In International Conference on Computer Vi-
sion.
Zhang, Z., Fidler, S., and Urtasun, R. (2016a). Instance-
level segmentation for autonomous driving with deep
densely connected mrfs. In Conference on Computer
Vision and Pattern Recognition, pages 669–677.
Zhang, Z., S., F., and R., U. (2016b). Instance-level segmen-
tation with deep densely connected MRFs. In Confe-
rence on Computer Vision and Pattern Recognition.
VISAPP 2018 - International Conference on Computer Vision Theory and Applications
378