Women in Computer Vision


BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation
Naina Dhingra,
Florian Ritter,
Andreas Kunz
[pdf] [supp]
[bibtex]
@InProceedings{Dhingra_2021_CVPR, author = {Dhingra, Naina and Ritter, Florian and Kunz, Andreas}, title = {BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2150-2159} }

Collaborative Image and Object Level Features for Image Colourisation
Rita Pucci,
Christian Micheloni,
Niki Martinel
[pdf]
[bibtex]
@InProceedings{Pucci_2021_CVPR, author = {Pucci, Rita and Micheloni, Christian and Martinel, Niki}, title = {Collaborative Image and Object Level Features for Image Colourisation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2160-2169} }

DeepDNet: Deep Dense Network for Depth Completion Task
Girish Hegde,
Tushar Pharale,
Soumya Jahagirdar,
Vaishakh Nargund,
Ramesh Ashok Tabib,
Uma Mudenagudi,
Basavaraja Vandrotti,
Ankit Dhiman
[pdf]
[bibtex]
@InProceedings{Hegde_2021_CVPR, author = {Hegde, Girish and Pharale, Tushar and Jahagirdar, Soumya and Nargund, Vaishakh and Tabib, Ramesh Ashok and Mudenagudi, Uma and Vandrotti, Basavaraja and Dhiman, Ankit}, title = {DeepDNet: Deep Dense Network for Depth Completion Task}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2190-2199} }

On the Robustness of Monte Carlo Dropout Trained With Noisy Labels
Purvi Goel,
Li Chen
[pdf] [arXiv]
[bibtex]
@InProceedings{Goel_2021_CVPR, author = {Goel, Purvi and Chen, Li}, title = {On the Robustness of Monte Carlo Dropout Trained With Noisy Labels}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2219-2228} }

RUIG: Realistic Underwater Image Generation Towards Restoration
Chaitra Desai,
Ramesh Ashok Tabib,
Sai Sudheer Reddy,
Ujwala Patil,
Uma Mudenagudi
[pdf]
[bibtex]
@InProceedings{Desai_2021_CVPR, author = {Desai, Chaitra and Tabib, Ramesh Ashok and Reddy, Sai Sudheer and Patil, Ujwala and Mudenagudi, Uma}, title = {RUIG: Realistic Underwater Image Generation Towards Restoration}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2181-2189} }

PointDCCNet: 3D Object Categorization Network Using Point Cloud Decomposition
Siddharth Katageri,
Sameer Kulmi,
Ramesh Ashok Tabib,
Uma Mudenagudi
[pdf]
[bibtex]
@InProceedings{Katageri_2021_CVPR, author = {Katageri, Siddharth and Kulmi, Sameer and Tabib, Ramesh Ashok and Mudenagudi, Uma}, title = {PointDCCNet: 3D Object Categorization Network Using Point Cloud Decomposition}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2200-2208} }

Contrastive Domain Adaptation
Mamatha Thota,
Georgios Leontidis
[pdf] [arXiv]
[bibtex]
@InProceedings{Thota_2021_CVPR, author = {Thota, Mamatha and Leontidis, Georgios}, title = {Contrastive Domain Adaptation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2209-2218} }

CNN-Based Morphological Decomposition of X-Ray Images for Details and Defects Contrast Enhancement
Tahani Madmad,
Nicolas Delinte,
Christophe De Vleeschouwer
[pdf]
[bibtex]
@InProceedings{Madmad_2021_CVPR, author = {Madmad, Tahani and Delinte, Nicolas and De Vleeschouwer, Christophe}, title = {CNN-Based Morphological Decomposition of X-Ray Images for Details and Defects Contrast Enhancement}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2170-2180} }