Leveraging Dark Knowledge for Intrinsic Multimodal Out-of-Distribution Detection

Abstract

Out-of-distribution (OOD) detection is crucial for the safe deployment of deep neural models in applications such as autonomous driving. With the emerging multimodal nature of modern applications, recent attention has shifted toward OOD detection in multimodal settings. However, current multimodal OOD detection methods fail to fully exploit the synergy among modalities: they treat all modalities equally, disregarding their varying detection performance, and they are unable to capture the diverse uncertainty information encoded at the logit level. In this paper, we propose to exploit the dark knowledge within unimodal experts as the key to revealing their synergy. To this end, we introduce a ..