Fetching the paper…
Reading the bibliography…
In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial context (large receptive fields) and fine spatial details (high resolution), both of which incur high computational costs.
Segmentation and recognition using structure from motion point clouds
Brostow, G.J., Shotton, J., Fauqueur, J., Cipolla, R.: · 2008
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: · 2013
Earlier work this paper cites.
Semantic mapping for mobile robotics tasks: A survey
Kostavelis, I., Gasteratos, A.: · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
Earlier work this paper cites.
Enet: A deep neural network architecture for real-time semantic segmentation
Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: · 2016
Earlier work this paper cites.
Attention to scale: Scale-aware semantic image segmentation
Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: · 2016
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
Yu, F., Koltun, V.: · 2016
Earlier work this paper cites.
Clockwork convnets for video semantic segmentation
Shelhamer, E., Rakelly, K., Hoffman, J., Darrell, T.: · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Earlier work this paper cites.
Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 2017
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u., Polosukhin, I.: · 2017
Earlier work this paper cites.
Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade
Li, X., Liu, Z., Luo, P., Change Loy, C., Tang, X.: · 2017
Earlier work this paper cites.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., Cipolla, R.: · 2017
Earlier work this paper cites.
Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Lin, G., Milan, A., Shen, C., Reid, I.: · 2017
Earlier work this paper cites.
Large kernel matters – improve semantic segmentation by global convolutional network
Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: · 2017
Earlier work this paper cites.
Deep feature flow for video recognition
Zhu, X., Xiong, Y., Dai, J., Yuan, L., Wei, Y.: · 2017
Earlier work this paper cites.
Full-resolution residual networks for semantic segmentation in street scenes
Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: · 2017
Earlier work this paper cites.
Semantic video cnns through representation warping
Gadde, R., Jampani, V., Gehler, P.V.: · 2017
Cited alongside, same era.
Video scene parsing with predictive feature learning
Jin, X., Li, X., Xiao, H., Shen, X., Lin, Z., Yang, J., Chen, Y., Dong, J., Liu, L., Jie, Z., et al.: · 2017
Cited alongside, same era.
Efficient convnet for real-time semantic segmentation
Romera, E., Alvarez, J.M., Bergasa, L.M., Arroyo, R.: · 2017
Cited alongside, same era.
Coco-stuff: Thing and stuff classes in context
Caesar, H., Uijlings, J., Ferrari, V.: · 2018
Cited alongside, same era.
Semantic segmentation from limited training data
Milan, A., Pham, T., Vijay, K., Morrison, D., Tow, A.W., Liu, L., Erskine, J., Grinover, R., Gurman, A., Hunn, T., et al.: · 2018
Cited alongside, same era.
Normalized cut loss for weakly-supervised CNN segmentation
Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.: · 2018
Dynamic video segmentation network
Xu, Y.S., Fu, T.J., Yang, H.K., Lee, C.Y.: · 2018
Later among the works it cites.
Semantic video segmentation by gated recurrent flow propagation
Nilsson, D., Sminchisescu, C.: · 2018
Later among the works it cites.
Real-time joint semantic segmentation and depth estimation using asymmetric annotations
Nekrasov, V., Dharmasiri, T., Spek, A., Drummond, T., Shen, C., Reid, I.: · 2019
Later among the works it cites.
Joint learning of instance and semantic segmentation for robotic pick-and-place with heavy occlusions in clutter
Wada, K., Okada, K., Inaba, M.: · 2019
Later among the works it cites.
Dual attention network for scene segmentation
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: · 2019
Later among the works it cites.
Dfnet: Semantic segmentation on panoramic images with dynamic loss weights and residual fusion block
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Long-term visual localization using semantically segmented images
Stenborg, E., Toft, C., Hammarstrand, L.: · 2018
Cited alongside, same era.
Automated process for incorporating drivable path into real-time semantic segmentation
Zhou, W., Worrall, S., Zyner, A., Nebot, E.: · 2018
Cited alongside, same era.
Deep semantic lane segmentation for mapless driving
Meyer, A., Salscheider, N.O., Orzechowski, P.F., Stiller, C.: · 2018
Cited alongside, same era.
Context encoding for semantic segmentation
Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., Agrawal, A.: · 2018
Cited alongside, same era.
Icnet for real-time semantic segmentation on high-resolution images
Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: · 2018
Cited alongside, same era.
Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation
Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., Hajishirzi, H.: · 2018
Cited alongside, same era.
Jiang, W., Wu, Y., Guan, L., Zhao, J.: · 2019
Later among the works it cites.
In defense of pre-trained imagenet architectures for real-time semantic segmentation of road-driving images
Orsic, M., Kreso, I., Bevandic, P., Segvic, S.: · 2019
Later among the works it cites.
Efficient segmentation: Learning downsampling near semantic boundaries
Marin, D., He, Z., Vajda, P., Chatterjee, P., Tsai, S., Yang, F., Boykov, Y.: · 2019
Later among the works it cites.
Seeing behind things: Extending semantic segmentation to occluded regions
Purkait, P., Zach, C., Reid, I.: · 2019
Later among the works it cites.
Fasterseg: Searching for faster real-time semantic segmentation
Chen, W., Gong, X., Liu, X., Zhang, Q., Li, Y., Wang, Z.: · 2019
Later among the works it cites.
Expectation-maximization attention networks for semantic segmentation
Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., Liu, H.: · 2019
Later among the works it cites.
Adaptive pyramid context network for semantic segmentation
He, J., Deng, Z., Zhou, L., Wang, Y., Qiao, Y.: · 2019
Later among the works it cites.
Asymmetric non-local neural networks for semantic segmentation
Zhu, Z., Xu, M., Bai, S., Huang, T., Bai, X.: · 2019
Later among the works it cites.
Ccnet: Criss-cross attention for semantic segmentation
Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: · 2019
Later among the works it cites.
Accel: A corrective fusion network for efficient semantic segmentation on video
Jain, S., Wang, X., Gonzalez, J.E.: · 2019
Later among the works it cites.
Shelfnet for fast semantic segmentation
Zhuang, J., Yang, J., Gu, L., Dvornek, N.: · 2019
Later among the works it cites.
Image segmentation using deep learning: A survey
Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: · 2020
Closest in time.
Temporally distributed networks for fast video semantic segmentation
Hu, P., Caba, F., Wang, O., Lin, Z., Sclaroff, S., Perazzi, F.: · 2020
Closest in time.
Efficient ladder-style densenets for semantic segmentation of large images
Krešo, I., Krapac, J., Šegvić, S.: · 2020
Closest in time.