Gala (Galadrielle Humblot-Renaux)

important cat

I’m a PhD student in the Visual Analysis & Perception lab @ Aalborg University, Denmark, under the supervision of Sergio Escalera & Thomas Moeslund. I have a background in engineering & robotic perception, and after receiving a scholarship from the Danish Data Science Academy, I am now working at the intersection of computer vision & uncertainty. I like questioning the trustworthiness of deep learning models & how we evaluate them. Lately I’ve been especially interested in the problems of out-of-distribution detection and data/label uncertainty.

Education:

MSc Robotics from Aalborg University, Denmark / Master thesis: “Where can I go?” Deep multi-modal scene understanding for outdoor navigation supervised by Rikke Gade & Letizia Marchegiani - I proposed a severity-aware learning scheme with soft labels and loss weighting, an adaptive multi-modal fusion module and an out-of-dataset evaluation procedure.
BSc Electrical Engineering from the University of Twente, Netherlands where I developed an interest in signal processing and dipped my toes in computer vision for the first time.

selected publications

CVPR’24
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?

Galadrielle Humblot-Renaux, Sergio Escalera, and Thomas B. Moeslund

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024

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The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems. In the context of classification, the task of detecting images outside of a model’s training domain is known as out-of-distribution (OOD) detection. While there has been a growing research interest in developing post-hoc OOD detection methods, there has been comparably little discussion around how these methods perform when the underlying classifier is not trained on a clean, carefully curated dataset. In this work, we take a closer look at 20 state-of-the-art OOD detection methods in the (more realistic) scenario where the labels used to train the underlying classifier are unreliable (e.g. crowd-sourced or web-scraped labels). Extensive experiments across different datasets, noise types & levels, architectures and checkpointing strategies provide insights into the effect of class label noise on OOD detection, and show that poor separation between incorrectly classified ID samples vs. OOD samples is an overlooked yet important limitation of existing methods. Code: https://github.com/glhr/ood-labelnoise
@inproceedings{Humblot-Renaux_2024_CVPR, title = {A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?}, author = {Humblot-Renaux, Galadrielle and Escalera, Sergio and Moeslund, Thomas B.}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2024}, eprint = {https://openaccess.thecvf.com/content/CVPR2024/html/Humblot-Renaux_A_Noisy_Elephant_in_the_Room_Is_Your_Out-of-Distribution_Detector_CVPR_2024_paper.html} }
CVPRW’23
Beyond AUROC & Co. for Evaluating Out-of-Distribution Detection Performance

Galadrielle Humblot-Renaux, Sergio Escalera, and Thomas B. Moeslund

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , Jun 2023

Abs Bib Code

While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://github.com/glhr/beyond-auroc
@inproceedings{Humblot-Renaux_2023_CVPRW, author = {Humblot-Renaux, Galadrielle and Escalera, Sergio and Moeslund, Thomas B.}, title = {Beyond AUROC \& Co. for Evaluating Out-of-Distribution Detection Performance}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, doi = {10.1109/CVPRW59228.2023.00402}, month = jun, year = {2023}, pages = {3881-3890}, }

ICRA’22

Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images

Galadrielle Humblot-Renaux, Letizia Marchegiani, Thomas B. Moeslund, and Rikke Gade

IEEE Robotics and Automation Letters, Jun 2022

Bib Supp

@article{Humblot-Renaux_2021_RAL,
  author = {Humblot-Renaux, Galadrielle and Marchegiani, Letizia and Moeslund, Thomas B. and Gade, Rikke},
  journal = {IEEE Robotics and Automation Letters},
  title = {Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images},
  year = {2022},
  volume = {7},
  number = {2},
  pages = {2913-2920},
  doi = {10.1109/LRA.2022.3144491},
}