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Dichotomous Image Segmentation

2D Object Detection › Object Detection › RGB Salient Object Detection › Dichotomous Image Segmentation 113 평가 기록 6 datasets

Currently, existing image segmentation tasks mainly focus on segmenting objects with specific characteristics, e.g., salient, camouflaged, meticulous, or specific categories. Most of them have the same input/output formats, and barely use exclusive mechanisms designed for segmenting targets in their models, which means almost all tasks are dataset-dependent. Thus, it is very promising to formulate a category-agnostic DIS task for accurately segmenting objects with different structure complexities, regardless of their characteristics. Compared with semantic segmentation, the proposed DIS task usually focuses on images with single or a few targets, from which getting richer accurate details of each target is more feasible.

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
BEN_Base
max F-Measure: 0.923weighted F-measure: 0.871MAE: 0.031S-Measure: 0.916
논문 코드
PDFNet
max F-Measure: 0.890weighted F-measure: 0.846MAE: 0.031S-Measure: 0.899
논문 코드
PDFNet
max F-Measure: 0.921weighted F-measure: 0.885MAE: 0.028S-Measure: 0.924
논문 코드
PDFNet
max F-Measure: 0.936weighted F-measure: 0.900MAE: 0.027S-Measure: 0.928
논문 코드
MVANet
max F-Measure: 0.912weighted F-measure: 0.857MAE: 0.041S-Measure: 0.903
논문 코드
UNet
AUC: 0.780
논문 코드

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6개

논문

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