Adriano Cardace

Orcid: 0000-0002-0584-3109

According to our database1, Adriano Cardace authored at least 13 papers between 2021 and 2024.

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

Timeline

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Links

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Bibliography

2024
Deep Learning on Object-Centric 3D Neural Fields.
IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

Neural Processing of Tri-Plane Hybrid Neural Fields.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

MMVR: Millimeter-Wave Multi-view Radar Dataset and Benchmark for Indoor Perception.
Proceedings of the Computer Vision - ECCV 2024, 2024

2023
Learning Good Features to Transfer Across Tasks and Domains.
IEEE Trans. Pattern Anal. Mach. Intell., August, 2023

Deep Learning on 3D Neural Fields.
CoRR, 2023

Boosting Multi-Modal Unsupervised Domain Adaptation for LiDAR Semantic Segmentation by Self-Supervised Depth Completion.
IEEE Access, 2023

Self-Distillation for Unsupervised 3D Domain Adaptation.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Permutation Equivariant Neural Functionals.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Learning on Implicit Neural Representations of Shapes.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

2021
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation.
Proceedings of the International Conference on 3D Vision, 2021


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