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MultiOOD is the first-of-its-kind benchmark for Multimodal OOD Detection, characterized by diverse dataset sizes and varying modality combinations.
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## MultiOOD Benchmark
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MultiOOD is based on five public action recognition datasets (HMDB51, UCF101, EPIC-Kitchens, HAC, and Kinetics-600).
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An overview of the proposed framework for Multimodal OOD Detection. We introduce A2D algorithm to encourage enlarging the prediction discrepancy across modalities. Additionally, we propose a novel outlier synthesis algorithm, NP-Mix, designed to explore broader feature spaces, which complements A2D to strengthen the OOD detection performance.
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## Code
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https://github.com/donghao51/MultiOOD
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## Contact
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If you have any questions, please send an email to donghaospurs@gmail.com
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MultiOOD is the first-of-its-kind benchmark for Multimodal OOD Detection, characterized by diverse dataset sizes and varying modality combinations.
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## Code
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https://github.com/donghao51/MultiOOD
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## MultiOOD Benchmark
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MultiOOD is based on five public action recognition datasets (HMDB51, UCF101, EPIC-Kitchens, HAC, and Kinetics-600).
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An overview of the proposed framework for Multimodal OOD Detection. We introduce A2D algorithm to encourage enlarging the prediction discrepancy across modalities. Additionally, we propose a novel outlier synthesis algorithm, NP-Mix, designed to explore broader feature spaces, which complements A2D to strengthen the OOD detection performance.
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## Contact
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If you have any questions, please send an email to donghaospurs@gmail.com
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