Portrait of Ge Li

Ge Li

ge.li@mila.quebec
Applied Research Scientist, Applied Machine Learning Research

Publications

CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset
Akshatha Arodi
Margaux Luck
Jean-Luc Bedwani
Aldo Zaimi
Ge Li
Nicolas Pouliot
Julien Beaudry
Gaétan Marceau Caron
Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to spec… (see more)ific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (VAD) for robotic power line inspection. While existing VAD methods perform well in controlled environments, real-world scenarios present diverse and unexpected anomalies that current datasets fail to capture. To address this gap, we introduce CableInspect-AD, a high-quality, publicly available dataset created and annotated by domain experts from Hydro-Québec, a Canadian public utility. This dataset includes high-resolution images with challenging real-world anomalies, covering defects with varying severity levels. To address the challenges of collecting diverse anomalous and nominal examples for setting a detection threshold, we propose an enhancement to the celebrated PatchCore algorithm. This enhancement enables its use in scenarios with limited labeled data. We also present a comprehensive evaluation protocol based on cross-validation to assess models' performances. We evaluate our Enhanced-PatchCore for few-shot and many-shot detection, and Vision-Language Models for zero-shot detection. While promising, these models struggle to detect all anomalies, highlighting the dataset's value as a challenging benchmark for the broader research community. Project page: https://mila-iqia.github.io/cableinspect-ad/.
2024-12-10
Neural Information Processing Systems (Accept (Poster))
doi.org
arxiv.org
Tracking Whale Calls in the Lower St. Lawrence Seaway at Land Seismometers
Eva Goblot
Yajing Liu
Alexandre Plourde
Pierre Cauchy
Jeanne Merindol
Coralie Bernier-Breton
Ge Li
Basile Roth
The Lower St. Lawrence Seaway (LSLS) is critical to Canada’s economy both as part of a major marine shipping corridor and a site of intens… (see more)ive fishing. Every year, fin whales and blue whales frequent the LSLS feeding ground. Understanding the mechanisms driving whale habitat usage is key for making informed decisions on shipping and fishing, reducing whale collision risks and mitigating noise pollution. We detect whales in the LSLS with land seismometers by using a method that relies on the intervals of the regularly repeating low frequency calls. The resulting catalogue contains 14,076 fin whale detections and 3,739 blue whale detections between February 2020 and January 2022. These detections follow the overall pattern of hydrophones, with most detections from fall to early winter in the Estuary and until mid-winter/spring in the Gulf. High detection rates in the Northwest Gulf throughout the winter months demonstrate that this region is potentially utilized year-round. This labelled catalogue may be suitable for developing a deep learning-based whale call detection algorithm. Making use of seismometers and deep learning can increase whale monitoring coverage within the LSLS and elsewhere.
2024-09-15
Seismica (published)
doi.org