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Attribute-Evidence Learning with Multimodal Prompt for Generalized Category Discovery
Author1, Author2, Author3, Author4, Author5
multimodalcategory discoveryattribute learningprompt reasoning
2611.12975
Builder Relevance
8h ago80%
Abstract
This paper presents a novel multimodal framework, AptM-GCD, for discovering novel categories from unlabeled data by leveraging textual guidance through attribute evidence learning and multimodal prompt reasoning.
Reality Card
Core Claim
AptM-GCD achieves state-of-the-art performance in generalized category discovery by effectively utilizing localized attribute evidence and multimodal prompts.
Method / Result
AptM-GCD demonstrates consistent state-of-the-art performance across six datasets.
Limitations
The reliance on specific attribute embeddings may limit generalizability to other datasets or domains.
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