Average Drop: 14.5Average Increase: 24.0ADCC: 59.4
Papers with Code · CC BY-SA 4.0
Feature Upsampling
Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime. However, these features often lack the spatial resolution to directly perform dense prediction tasks like segmentation and depth prediction because models aggressively pool information over large areas. Feature Upsampling aims to recover this missing spatial resolution without impacting the space of the original deep features.
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
벤치마크
Papers with Code 아카이브의 데이터셋별 기록 예시입니다. 평가 조건과 지표 방향이 검증되지 않아 순위를 의미하지 않습니다.
데이터셋
1개논문
Average Drop: 49.9Average Increase: 4.8ADCC: 59.7
Average Drop: 15.3Average Increase: 24.0ADCC: 64.3
Average Drop: 17.4Average Increase: 30.9ADCC: 73.3
Average Drop: 17.8Average Increase: 20.0ADCC: 61.6
Average Drop: 66.9Average Increase: 8.7ADCC: 53.0
Average Drop: 8.5Average Increase: 32.7ADCC: 55.4
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.