8 citations · 8 across the 5 of their papers we have counts for
6 papers
Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation
Tiankai Chen, Yushu Li, Adam Goodge +4
Out-of-distribution (OOD) detection in 3D point cloud data remains a challenge, particularly in applications where safe and robust perception is critical. While existing OOD detect…
SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation
Adam Goodge, Xun Xu, Bryan Hooi +4
As point cloud data increases in prevalence in a variety of applications, the ability to detect out-of-distribution (OOD) point cloud objects becomes critical for ensuring model sa…
Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities
Adam Goodge, Wee Siong Ng, Bryan Hooi +1
Foundation models have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and lang…
When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detection
Adam Goodge, Bryan Hooi, Wee Siong Ng
Contrastive Language-Image Pre-training (CLIP) achieves remarkable performance in various downstream tasks through the alignment of image and text input embeddings and holds great…
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring
Adam Goodge, Bryan Hooi, See Kiong Ng +1
How can we detect anomalies: that is, samples that significantly differ from a given set of high-dimensional data, such as images or sensor data? This is a practical problem with n…
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks
Adam Goodge, Bryan Hooi, See Kiong Ng +1
Many well-established anomaly detection methods use the distance of a sample to those in its local neighbourhood: so-called `local outlier methods', such as LOF and DBSCAN. They ar…