Dvir Ginzburg

According to our database1, Dvir Ginzburg authored at least 13 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Selective sampling with Gromov-Hausdorff metric: Efficient dense-shape correspondence via Confidence-based sample consensus.
Virtual Real. Intell. Hardw., 2024

2022
Interpreting BERT-based Text Similarity via Activation and Saliency Maps.
Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Shape-Consistent Generative Adversarial Networks for Multi-Modal Medical Segmentation Maps.
Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022

Deep Weighted Consensus Dense Correspondence Confidence Maps for 3d Shape Registration.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

Metricbert: Text Representation Learning Via Self-Supervised Triplet Training.
Proceedings of the IEEE International Conference on Acoustics, 2022

Deep Confidence Guided Distance for 3D Partial Shape Registration.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Spectral Teacher for a Spatial Student: Spectrum-Aware Real-Time Dense Shape Correspondence.
Proceedings of the International Conference on 3D Vision, 2022

2021
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference.
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction.
Proceedings of the International Conference on 3D Vision, 2021

Dual Geometric Graph Network (DG2N) Iterative Network for Deformable Shape Alignment.
Proceedings of the International Conference on 3D Vision, 2021

2020
Unsupervised Scale-Invariant Multispectral Shape Matching.
CoRR, 2020

Dual Geometric Graph Network (DG2N) - Zero-Shot Refinement for Dense Shape Correspondence.
CoRR, 2020

Cyclic Functional Mapping: Self-supervised Correspondence Between Non-isometric Deformable Shapes.
Proceedings of the Computer Vision - ECCV 2020, 2020


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