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Semantic segmentation empowers numerous real-world applications, such as autonomous driving and augmented/mixed reality.
Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. TPAMI (2000)
2000
Earlier work this paper cites.
Boykov, Y., Veksler, O., Zabih, R.: Fast Approximate Energy Minimization via Graph Cuts. TPAMI (2001)
2001
Earlier work this paper cites.
Lafferty, J., McCallum, A., Pereira, F.C.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: ICML (2001)
2001
Earlier work this paper cites.
Blake, A., Rother, C., Brown, M., Perez, P., Torr, P.: Interactive Image Segmentation Using an Adaptive GMMRF Model. In: ECCV (2004)
2004
Earlier work this paper cites.
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-based Image Segmentation. IJCV (2004)
2004
Earlier work this paper cites.
Rother, C., Kolmogorov, V., Blake, A.: “GrabCut” – Interactive Foreground Extraction Using Iterated Graph Cuts. In: SIGGRAPH (2004)
2004
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International journal of computer vision 88
2010
Earlier work this paper cites.
Krähenbühl, P., Koltun, V.: Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. In: NeurIPS (2011)
2011
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Kokkinos, K., Murphy, K., Yuille, A.L.: Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. In: ICLR (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Spatial Pyramid Pooling in Deep Convolutional Neural Networks for Visual Recognition. TPAMI (2015)
2015
Earlier work this paper cites.
Long, J., Shelhamer, E., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation. In: CVPR (2015)
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: MICCAI (2015)
2015
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Kokkinos, K., Murphy, K., Yuille, A.L.: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. TPAMI (2016)
2016
Earlier work this paper cites.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The Cityscapes Dataset for Semantic Urban Scene Understanding. In: CVPR (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and < < 0.5MB Model Size. arXiv (2016)
2016
Earlier work this paper cites.
Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. arXiv (2016)
2016
Earlier work this paper cites.
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. TPAMI (2017)
2017
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Deeplabv3: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: CVPR (2017)
2017
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous Convolution for Semantic Image Segmentation. In: CVPR (2017)
2017
Earlier work this paper cites.
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv (2017)
2017
Earlier work this paper cites.
Lin, G., Milan, A., Shen, C., Reid, I.: Refinenet: Multi-path Refinement Networks for High-resolution Semantic Segmentation. In: CVPR (2017)
2017
Earlier work this paper cites.
Loshchilov, I., Hutter, F.: SGDR: Stochastic Gradient Descent with Warm Restarts. In: ICLR (2017)
2017
Earlier work this paper cites.
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In: CVPR (2017)
2017
Earlier work this paper cites.
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid Scene Parsing Network. In: CVPR (2017)
2017
Earlier work this paper cites.
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Deeplabv3+: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: ECCV (2018)
2018
Earlier work this paper cites.
Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al.: Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC). In: ISBI (2018)
2018
Earlier work this paper cites.
Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., Raskar, R.: DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. In: CVPR Workshop (2018)
2018
Earlier work this paper cites.
Graham, B., Engelcke, M., van der Maaten, L.: 3D Semantic Segmentation With Submanifold Sparse Convolutional Networks. In: CVPR (2018)
2018
Earlier work this paper cites.
Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: Convolution on 𝒳 \mathcal{X} -Transformed Points. In: NeurIPS (2018)
2018
Earlier work this paper cites.
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. In: ECCV (2018)
2018
Earlier work this paper cites.
Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., Hajishirzi, H.: ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation. In: ECCV (2018)
2018
Earlier work this paper cites.
Pan, B., Lin, W., Fang, X., Huang, C., Zhou, B., Lu, C.: Recurrent Residual Module for Fast Inference in Videos. In: CVPR (2018)
2018
Earlier work this paper cites.
Ren, M., Pokrovsky, A., Urtasun, R.: SBNet: Sparse Blocks Network for Fast Inference. In: CVPR (2018)
2018
Earlier work this paper cites.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: CVPR (2018)
2018
Earlier work this paper cites.
Wu, B., Wan, A., Yue, X., Keutzer, K.: SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. In: ICRA (2018)
2018
Cited alongside, same era.
Yan, Y., Mao, Y., Li, B.: SECOND: Sparsely Embedded Convolutional Detection. Sensors (2018)
2018
Cited alongside, same era.
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation. In: ECCV (2018)
2018
Cited alongside, same era.
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In: CVPR (2018)
2018
Cited alongside, same era.
Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: ICNet for Real-Time Semantic Segmentation on High-Resolution Images. In: ECCV (2018)
2018
Cited alongside, same era.
Wang, H., Zhang, Z., Han, S.: SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning. In: HPCA (2021)
2021
Later among the works it cites.
Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction Without Convolutions. In: ICCV (2021)
2021
Later among the works it cites.
Wang, Y., Zhang, C., Xie, Z., Guo, C., Liu, Y., Leng, J.: Dual-side Sparse Tensor Core. In: ISCA (2021)
2021
Later among the works it cites.
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. In: NeurIPS (2021)
2021
Later among the works it cites.
Yin, H., Vahdat, A., Alvarez, J., Mallya, A., Kautz, J., Molchanov, P.: AdaViT: Adaptive Tokens for Efficient Vision Transformer. In: CVPR (2021)
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Chen, Y., Fan, H., Xu, B., Yan, Z., Kalantidis, Y., Rohrbach, M., Yan, S., Feng, J.: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution. In: ICCV (2019)
2019
Cited alongside, same era.
Choy, C., Gwak, J., Savarese, S.: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. In: CVPR (2019)
2019
Cited alongside, same era.
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual Attention Network for Scene Segmentation. In: CVPR (2019)
2019
Cited alongside, same era.
Gondimalla, A., Chesnut, N., Thottethodi, M., Vijaykumar, T.: SparTen: A Sparse Tensor Accelerator for Convolutional Neural Networks. In: MICRO (2019)
2019
Cited alongside, same era.
Huang, Y.H., Proesmans, M., Georgoulis, S., Van Gool, L.: Uncertainty Based Model Selection for Fast Semantic Segmentation. In: MVA (2019)
2019
Cited alongside, same era.
Kirillov, A., Girshick, R., He, K., Dollár, P.: Panoptic Feature Pyramid Networks. In: CVPR (2019)
2019
Cited alongside, same era.
Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A., Fei-Fei, L.: Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation. In: CVPR (2019)
2019
Cited alongside, same era.
2021
Later among the works it cites.
Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., Sang, N.: BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation. IJCV (2021)
2021
Later among the works it cites.
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z.H., Tay, F.E., Feng, J., Yan, S.: Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet. In: ICCV (2021)
2021
Later among the works it cites.
Yuan, Y., Fu, R., Huang, L., Lin, W., Zhang, C., Chen, X., Wang, J.: HRFormer: High-Resolution Transformer for Dense Prediction. In: NeurIPS (2021)
2021
Later among the works it cites.
Zhang, G., Lu, X., Tan, J., Li, J., Zhang, Z., Li, Q., Hu, X.: RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features. In: CVPR (2021)
2021
Later among the works it cites.
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., Zhang, L.: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers. In: CVPR (2021)
2021
Later among the works it cites.
Cai, H., Gan, C., Han, S.: EfficientViT: Lightweight Multi-Scale Attention for On-Device Semantic Segmentation. arXiv (2022)
2022
Later among the works it cites.
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation. In: ECCVW (2022)
2022
Later among the works it cites.
Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention Mask Transformer for Universal Image Segmentation. In: CVPR (2022)
2022
Later among the works it cites.
Fan, L., Pang, Z., Zhang, T., Wang, Y.X., Zhao, H., Wang, F., Wang, N., Zhang, Z.: Embracing Single Stride 3D Object Detector with Sparse Transformer. In: CVPR (2022)
2022
Later among the works it cites.
Gao, P., Ma, T., Li, H., Dai, J., Qiao, Y.: ConvMAE: Masked Convolution Meets Masked Autoencoders. In: NeurIPS (2022)
2022
Later among the works it cites.
Guo, M.H., Lu, C.Z., Hou, Q., Liu, Z., Cheng, M.M., Hu, S.M.: SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation. In: NeurIPS (2022)
2022
Later among the works it cites.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked Autoencoders Are Scalable Vision Learners. In: CVPR (2022)
2022
Later among the works it cites.
Huang, L., You, S., Zheng, M., Wang, F., Qian, C., Yamasaki, T.: Green Hierarchical Vision Transformer for Masked Image Modeling. In: NeurIPS (2022)
2022
Later among the works it cites.
Kong, Z., Dong, P., Ma, X., Meng, X., Niu, W., Sun, M., Ren, B., Qin, M., Tang, H., Wang, Y.: SPViT: Enabling Faster Vision Transformers via Soft Token Pruning. In: ECCV (2022)
2022
Later among the works it cites.
Liang, Y., Ge, C., Tong, Z., Song, Y., Wang, J., Xie, P.: Not All Patches Are What You Need: Expediting Vision Transformers via Token Reorganizations. In: ICLR (2022)
2022
Later among the works it cites.
Liu, J., Chen, Y., Ye, X., Tian, Z., Tan, X., Qi, X.: Spatial Pruned Sparse Convolution for Efficient 3D Object Detection. In: NeurIPS (2022)
2022
Later among the works it cites.
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al.: Swin Transformer v2: Scaling Up Capacity and Resolution. In: CVPR (2022)
2022
Later among the works it cites.
Song, Z., Xu, Y., He, Z., Jiang, L., Jing, N., Liang, X.: CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction. arXiv (2022)
2022
Later among the works it cites.
Sun, P., Tan, M., Wang, W., Liu, C., Xia, F., Leng, Z., Anguelov, D.: SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds. In: ECCV (2022)
2022
Later among the works it cites.
Tang, H., Liu, Z., Li, X., Lin, Y., Han, S.: TorchSparse: Efficient Point Cloud Inference Engine. In: MLSys (2022)
2022
Later among the works it cites.
Verelst, T., Tuytelaars, T.: SegBlocks: Block-Based Dynamic Resolution Networks for Real-Time Segmentation. TPAMI (2022)
2022
Later among the works it cites.
Bolya, D., Fu, C.Y., Dai, X., Zhang, P., Feichtenhofer, C., Hoffman, J.: Token Merging: Your ViT but Faster. In: ICLR (2023)
2023
Later among the works it cites.
Bolya, D., Hoffman, J.: Token Merging for Fast Stable Diffusion. arXiv (2023)
2023
Later among the works it cites.
Chen, X., Liu, Z., Tang, H., Yi, L., Zhao, H., Han, S.: SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer. In: CVPR (2023)
2023
Later among the works it cites.
Hong, K., Yu, Z., Dai, G., Yang, X., Lian, Y., Liu, Z., Xu, N., Wang, Y.: Exploiting Hardware Utilization and Adaptive Dataflow for Efficient Sparse Convolution in 3D Point Clouds. In: MLSys (2023)
2023
Later among the works it cites.
Liu, Z., Yang, X., Tang, H., Yang, S., Han, S.: FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer. In: CVPR (2023)
2023
Later among the works it cites.
Ma, X., Zhou, Y., Wang, H., Qin, C., Sun, B., Liu, C., Fu, Y.: Image as Set of Points. In: ICLR (2023)
2023
Later among the works it cites.
Tang, H., Yang, S., Liu, Z., Hong, K., Yu, Z., Li, X., Dai, G., Wang, Y., Han, S.: TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs. In: MICRO (2023)
2023
Later among the works it cites.
Tian, K., Jiang, Y., Diao, Q., Lin, C., Wang, L., Yuan, Z.: Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling. In: ICLR (2023)
2023
Later among the works it cites.
Wang, H., Shi, C., Shi, S., Lei, M., Wang, S., He, D., Schiele, B., Wang, L.: DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets. In: CVPR (2023)
2023
Later among the works it cites.