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MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid. (arXiv:2211.05232v1 [cs.CV])
Nov. 11, 2022, 2:14 a.m. | Fengjun Wang, Sarai Mizrachi, Moran Beladev, Guy Nadav, Gil Amsalem, Karen Lastmann Assaraf, Hadas Harush Boker
cs.CV updates on arXiv.org arxiv.org
Multi-label image classification is a foundational topic in various domains.
Multimodal learning approaches have recently achieved outstanding results in
image representation and single-label image classification. For instance,
Contrastive Language-Image Pretraining (CLIP) demonstrates impressive
image-text representation learning abilities and is robust to natural
distribution shifts. This success inspires us to leverage multimodal learning
for multi-label classification tasks, and benefit from contrastively learnt
pretrained models. We propose the Multimodal Multi-label Image Classification
(MuMIC) framework, which utilizes a hardness-aware tempered sigmoid based
Binary …
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