MULTIMODAL FEW-SHOT CLASSIFICATION WITHOUT ATTRIBUTE EMBEDDING

Multimodal few-shot classification without attribute embedding

Abstract Multimodal few-shot learning aims to exploit complementary information inherent in multiple modalities for vision tasks in low data scenarios.Most of the current research focuses on a suitable embedding space for the various modalities.While solutions based on embedding provide state-of-the-art results, they reduce the interpretability of

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Plantago psyllium-secondary anaphylaxis. Case report

Background: Psyllium is a derivative of Plantago ovata ground seed and husk that is used as bulk-forming laxatives owing to its hydrocolloid properties.Case report: 43-year-old female nurse with previous diagnosis of drug allergy and allergic CALENDULA rhinitis who, after the preparation and administration of a laxative, developed rhinoconjunctivit

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