May 27, 2024 · We introduce the first-of-its-kind benchmark, MultiOOD, characterized by diverse dataset sizes and varying modality combinations.
MultiOOD is the first-of-its-kind benchmark for Multimodal OOD Detection, characterized by diverse dataset sizes and varying modality combinations. MultiOOD ...
Nov 5, 2024 · This paper looks at the problem of out-of-distribution detection in multi-modal settings, presenting a new benchmark MultiOOD that has a range ...
To establish a foundation for more realistic Multimodal OOD Detection, we introduce the first-of-its-kind benchmark, MultiOOD, characterized by diverse dataset ...
May 27, 2024 · This work introduces the first-of-its-kind benchmark, MultiOOD, characterized by diverse dataset sizes and varying modality combinations, ...
In this paper, we introduce the Multimodal Out-of-distribution Detection problem and present a novel benchmark, MultiOOD, which includes diverse dataset sizes ...
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May 29, 2024 · Check out our recent work "MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities". Current research in OOD detection has ...
Detecting out-of-distribution (OOD) samples is important for deployingmachine learning models in safety-critical applications such as ...
title = {{MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities}},. journal = {arXiv preprint arXiv:2405.17419},. year = {2024},. }.
May 27, 2024 · We conduct comprehensive evaluations of existing unimodal OOD algorithms on MultiOOD,. revealing their limitations in multimodal scenarios.