GeneCIS
GeneCIS benchmark is designed for measuring models’ ability to adapt to a range of similarity conditions, which is zero-shot evaluation only.
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The Zero-shot Composed Person Retrieval (ZS-CPR) task aims to jointly utilize visual and textual information for retrieving specific individuals. Traditional person retrieval methods, whether image-based or text-based, often struggle to effectively leverage both types of information, resulting in reduced accuracy. To address this, the Composed Person Retrieval (CPR) task is introduced, which combines image and text information for target person retrieval. However, supervised CPR methods require costly manual annotation datasets that are currently unavailable. To overcome this, the ZS-CPR task is proposed, which leverages existing domain-related data to solve the CPR problem without relying on expensive annotations.
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GeneCIS benchmark is designed for measuring models’ ability to adapt to a range of similarity conditions, which is zero-shot evaluation only.
The ITCPR dataset is a comprehensive collection specifically designed for the Zero-Shot Composed Person Retrieval (ZS-CPR) task. It consists of a total of 2,225 annotated triplets, derived from three distinct datasets: Celeb-reID, PRCC, and LAST.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.