The iMet Collection 2019 Challenge Dataset
원문 초록
Existing computer vision technologies in artwork recognition focus mainly on instance retrieval or coarse-grained attribute classification. In this work, we present a novel dataset for fine-grained artwork attribute recognition. The images in the dataset are professional photographs of classic artworks from the Metropolitan Museum of Art, and annotations are curated and verified by world-class museum experts. In addition, we also present the iMet Collection 2019 Challenge as part of the FGVC6 workshop. Through the competition, we aim to spur the enthusiasm of the fine-grained visual recognition research community and advance the state-of-the-art in digital curation of museum collections.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Adding simple structure at inference improves Vision-Language Compositionality
An analysis of vision-language models for fabric retrieval
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Zero-Shot Learning Based Approach For Medieval Word Recognition Using Deep-Learned Features
Label Consistent Fisher Vectors for Supervised Feature Aggregation
From Roots to Rewards: Dynamic Tree Reasoning with RL
토론
이 논문과 연결된 포럼 글이 아직 없습니다.