Mattia Segù

Orcid: 0000-0002-9107-531X

According to our database1, Mattia Segù authored at least 18 papers between 2020 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking.
CoRR, 2024

Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs.
Proceedings of the Computer Vision - ECCV 2024, 2024

SLAck: Semantic, Location, and Appearance Aware Open-Vocabulary Tracking.
Proceedings of the Computer Vision - ECCV 2024, 2024

UniDepth: Universal Monocular Metric Depth Estimation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Matching Anything by Segmenting Anything.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Batch normalization embeddings for deep domain generalization.
Pattern Recognit., 2023

Towards Robust Object Detection Invariant to Real-World Domain Shifts.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

DARTH: Holistic Test-time Adaptation for Multiple Object Tracking.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking.
Proceedings of the Pattern Recognition - 45th DAGM German Conference, 2023

2022
Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts.
CoRR, 2022

On the Practicality of Deterministic Epistemic Uncertainty.
Proceedings of the International Conference on Machine Learning, 2022

Generative Cooperative Learning for Unsupervised Video Anomaly Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
On the Practicality of Deterministic Epistemic Uncertainty.
CoRR, 2021

2020
A General Framework for Uncertainty Estimation in Deep Learning.
IEEE Robotics Autom. Lett., 2020

Depth-Aware Action Recognition: Pose-Motion Encoding through Temporal Heatmaps.
CoRR, 2020

3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer.
CoRR, 2020


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