June 5, 2024, 4:49 a.m. | Yunpeng Zhao, Cheng Chen, Qing You Pang, Quanzheng Li, Carol Tang, Beng-Ti Ang, Yueming Jin

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.01987v1 Announce Type: new
Abstract: Addressing missing modalities presents a critical challenge in multimodal learning. Current approaches focus on developing models that can handle modality-incomplete inputs during inference, assuming that the full set of modalities are available for all the data during training. This reliance on full-modality data for training limits the use of abundant modality-incomplete samples that are often encountered in practical settings. In this paper, we propose a robust universal model with modality reconstruction and model personalization, which …

abstract arxiv challenge cs.cv current data focus inference inputs multimodal multimodal learning personalization reliance robust set stage training type universal universal model

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