4 papers
Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection
Xiang Fang, Arvind Easwaran, Blaise Genest
Out-of-distribution (OOD) detection attempts to distinguish outlier samples to prevent models trained on the in-distribution (ID) dataset from producing unavailable outputs. Most O…
Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection
Xiang Fang, Arvind Easwaran, Blaise Genest +1
This paper focuses on a significant yet challenging task: out-of-distribution detection (OOD detection), which aims to distinguish and reject test samples with semantic shifts, so…
Your Data Is Not Perfect: Towards Cross-Domain Out-of-Distribution Detection in Class-Imbalanced Data
Xiang Fang, Arvind Easwaran, Blaise Genest +1
Previous OOD detection systems only focus on the semantic gap between ID and OOD samples. Besides the semantic gap, we are faced with two additional gaps: the domain gap between so…
Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection
Xiang Fang, Arvind Easwaran, Blaise Genest
Out-of-distribution (OOD) detection targets to detect and reject test samples with semantic shifts, to prevent models trained on in-distribution (ID) dataset from producing unrelia…