3 papers
cs.CV2026
Understanding and Overcoming Cross-modal Fusion Bias in Multimodal Anomaly Detection From A Fisher Information Perspective
Kaifang Long, Lianbo Ma, Liming Liu +1
Current advancements in Multimodal Anomaly Detection (MAD) are largely driven by enhancing multimodal fusion, particularly through the integration of RGB and Depth data for richer…
cs.CV2026
Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective
Kaifang Long, Lianbo Ma, Jiaqi Liu +2
The quest for incremental unified multimodal anomaly detection seeks to empower a single model with the ability to systematically detect anomalies across all categories and support…
cs.CV2024
Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective
Kaifang Long, Guoyang Xie, Lianbo Ma +2
Existing efforts to boost multimodal fusion of 3D anomaly detection (3D-AD) primarily concentrate on devising more effective multimodal fusion strategies. However, little attention…