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Found 2 papers, 2 papers with code
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning
In addition, we unify the input representation of multi-modality into a 2D image format, enabling multi-modal anomaly detection and reasoning.
Customizable Perturbation Synthesis for Robust SLAM Benchmarking
To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations.
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