publications

2022

1. TPAMI

   Action Recognition From a Single Coded Image

   Sudhakar Kumawat, Tadashi Okawara, Michitaka Yoshida, Hajime Nagahara, and Yasushi Yagi

   IEEE Transactions on Pattern Analysis and Machine Intelligence 2022

   Bib PDF

   @article{9850412,
     bibtex_show = {true},
     abbr = {TPAMI},
     pdf = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9850412},
     author = {Kumawat, Sudhakar and Okawara, Tadashi and Yoshida, Michitaka and Nagahara, Hajime and Yagi, Yasushi},
     journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     title = {Action Recognition From a Single Coded Image},
     year = {2022},
     volume = {},
     number = {},
     pages = {1-14},
     doi = {10.1109/TPAMI.2022.3196350}
   }

2. ECCV

   Privacy-Preserving Action Recognition via Motion Difference Quantization

   Sudhakar Kumawat, and Hajime Nagahara

   In European Conference on Computer Vision 2022

   Bib PDF

   @inproceedings{kumawat2022privacy,
     bibtex_show = {true},
     abbr = {ECCV},
     pdf = {https://arxiv.org/abs/2208.02459},
     title = {Privacy-Preserving Action Recognition via Motion Difference Quantization},
     author = {Kumawat, Sudhakar and Nagahara, Hajime},
     booktitle = {European Conference on Computer Vision},
     pages = {518--534},
     year = {2022},
     organization = {Springer}
   }

3. NCC

   ShuffleBlock: Shuffle to Regularize Convolutional Neural Networks

   Sudhakar Kumawat, Gagan Kanojia, and Shanmuganathan Raman

   In 2022 National Conference on Communications (NCC) 2022

   Bib PDF

   @inproceedings{9806750,
     bibtex_show = {true},
     abbr = {NCC},
     pdf = {https://arxiv.org/pdf/2106.09358.pdf},
     author = {Kumawat, Sudhakar and Kanojia, Gagan and Raman, Shanmuganathan},
     booktitle = {2022 National Conference on Communications (NCC)},
     title = {ShuffleBlock: Shuffle to Regularize Convolutional Neural Networks},
     year = {2022},
     volume = {},
     number = {},
     pages = {36-41},
     doi = {10.1109/NCC55593.2022.9806750}
   }

2021

1. TPAMI

   Depthwise spatio-temporal STFT convolutional neural networks for human action recognition

   Sudhakar Kumawat, Manisha Verma, Yuta Nakashima, and Shanmuganathan Raman

   IEEE Transactions on Pattern Analysis and Machine Intelligence 2021

   Bib PDF

   @article{kumawat2021depthwise,
     bibtex_show = {true},
     abbr = {TPAMI},
     pdf = {https://ieeexplore.ieee.org/iel7/34/4359286/09419752.pdf},
     title = {Depthwise spatio-temporal STFT convolutional neural networks for human action recognition},
     author = {Kumawat, Sudhakar and Verma, Manisha and Nakashima, Yuta and Raman, Shanmuganathan},
     journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     year = {2021},
     publisher = {IEEE}
   }

2020

1. ICASSP

   Depthwise-STFT based separable convolutional neural networks

   Sudhakar Kumawat, and Shanmuganathan Raman

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020

   Bib PDF

   @inproceedings{kumawat2020depthwise,
     bibtex_show = {true},
     abbr = {ICASSP},
     pdf = {https://ieeexplore.ieee.org/iel7/9040208/9052899/09053898.pdf},
     title = {Depthwise-STFT based separable convolutional neural networks},
     author = {Kumawat, Sudhakar and Raman, Shanmuganathan},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     pages = {3337--3341},
     year = {2020},
     organization = {IEEE}
   }

2. CVPR-w

   Yoga-82: a new dataset for fine-grained classification of human poses

   Manisha Verma,  Sudhakar Kumawat, Yuta Nakashima, and Shanmuganathan Raman

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 2020

   Bib PDF Website

   @inproceedings{verma2020yoga,
     bibtex_show = {true},
     website = {https://sites.google.com/view/yoga-82/home},
     abbr = {CVPR-w},
     pdf = {http://openaccess.thecvf.com/content_CVPRW_2020/papers/w70/Verma_Yoga-82_A_New_Dataset_for_Fine-Grained_Classification_of_Human_Poses_CVPRW_2020_paper.pdf},
     title = {Yoga-82: a new dataset for fine-grained classification of human poses},
     author = {Verma, Manisha and Kumawat, Sudhakar and Nakashima, Yuta and Raman, Shanmuganathan},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
     pages = {1038--1039},
     year = {2020}
   }

3. ACM CoDs

   PlantDoc: a dataset for visual plant disease detection

   Davinder Singh, Naman Jain, Pranjali Jain, Pratik Kayal,  Sudhakar Kumawat, and Nipun Batra

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 2020

   Bib PDF Website

   @incollection{singh2020plantdoc,
     bibtex_show = {true},
     website = {https://github.com/pratikkayal/PlantDoc-Dataset},
     abbr = {ACM CoDs},
     pdf = {https://dl.acm.org/doi/pdf/10.1145/3371158.3371196},
     title = {PlantDoc: a dataset for visual plant disease detection},
     author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},
     booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},
     pages = {249--253},
     year = {2020}
   }

2019

1. NCVPRIPG

   Exploring temporal differences in 3D convolutional neural networks

   Gagan Kanojia,  Sudhakar Kumawat, and Shanmuganathan Raman

   In National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics 2019

   Bib PDF Website

   @inproceedings{kanojia2019exploring,
     bibtex_show = {true},
     website = {https://github.com/GaganKanojia/SSA-ResNet},
     abbr = {NCVPRIPG},
     pdf = {https://arxiv.org/pdf/1909.03309},
     title = {Exploring temporal differences in 3D convolutional neural networks},
     author = {Kanojia, Gagan and Kumawat, Sudhakar and Raman, Shanmuganathan},
     booktitle = {National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics},
     pages = {111--121},
     year = {2019},
     organization = {Springer}
   }

2. CVPR

   Lp-3dcnn: Unveiling local phase in 3d convolutional neural networks

   Sudhakar Kumawat, and Shanmuganathan Raman

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019

   Bib PDF

   @inproceedings{kumawat2019lp,
     bibtex_show = {true},
     abbr = {CVPR},
     pdf = {http://openaccess.thecvf.com/content_CVPR_2019/papers/Kumawat_LP-3DCNN_Unveiling_Local_Phase_in_3D_Convolutional_Neural_Networks_CVPR_2019_paper.pdf},
     title = {Lp-3dcnn: Unveiling local phase in 3d convolutional neural networks},
     author = {Kumawat, Sudhakar and Raman, Shanmuganathan},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
     pages = {4903--4912},
     year = {2019}
   }

3. CVPR-w

   Attentive spatio-temporal representation learning for diving classification

   Gagan Kanojia,  Sudhakar Kumawat, and Shanmuganathan Raman

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 2019

   Bib PDF Website

   @inproceedings{kanojia2019attentive,
     bibtex_show = {true},
     website = {https://github.com/GaganKanojia/Attentive-spatio-temporal-representation-learning-for-diving-classification},
     abbr = {CVPR-w},
     pdf = {http://openaccess.thecvf.com/content_CVPRW_2019/papers/CVSports/Kanojia_Attentive_Spatio-Temporal_Representation_Learning_for_Diving_Classification_CVPRW_2019_paper.pdf},
     title = {Attentive spatio-temporal representation learning for diving classification},
     author = {Kanojia, Gagan and Kumawat, Sudhakar and Raman, Shanmuganathan},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
     pages = {0--0},
     year = {2019}
   }

4. CVPR-w

   LBVCNN: Local binary volume convolutional neural network for facial expression recognition from image sequences

   Sudhakar Kumawat, Manisha Verma, and Shanmuganathan Raman

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 2019

   Bib PDF

   @inproceedings{kumawat2019lbvcnn,
     bibtex_show = {true},
     abbr = {CVPR-w},
     pdf = {http://openaccess.thecvf.com/content_CVPRW_2019/papers/AMFG/Kumawat_LBVCNN_Local_Binary_Volume_Convolutional_Neural_Network_for_Facial_Expression_CVPRW_2019_paper.pdf},
     title = {LBVCNN: Local binary volume convolutional neural network for facial expression recognition from image sequences},
     author = {Kumawat, Sudhakar and Verma, Manisha and Raman, Shanmuganathan},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
     pages = {0--0},
     year = {2019}
   }

5. ICASSP

   Local phase U-Net for fundus image segmentation

   Sudhakar Kumawat, and Shanmuganathan Raman

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019

   Bib PDF

   @inproceedings{kumawat2019local,
     bibtex_show = {true},
     abbr = {ICASSP},
     pdf = {https://ieeexplore.ieee.org/iel7/8671773/8682151/08683390.pdf},
     title = {Local phase U-Net for fundus image segmentation},
     author = {Kumawat, Sudhakar and Raman, Shanmuganathan},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     pages = {1209--1213},
     year = {2019},
     organization = {IEEE}
   }

2017

1. Inscrypt

   A new constant-size accountable ring signature scheme without random oracles

   Sudhakar Kumawat, and Souradyuti Paul

   In International Conference on Information Security and Cryptology 2017

   Bib PDF

   @inproceedings{kumawat2017new,
     bibtex_show = {true},
     abbr = {Inscrypt},
     pdf = {https://eprint.iacr.org/2018/242.pdf},
     title = {A new constant-size accountable ring signature scheme without random oracles},
     author = {Kumawat, Sudhakar and Paul, Souradyuti},
     booktitle = {International Conference on Information Security and Cryptology},
     pages = {157--179},
     year = {2017},
     organization = {Springer}
   }