S-Measure: 91.5Average MAE: 0.039max E-Measure: 94.8max F-Measure: 92.0
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RGB-D Salient Object Detection
RGB-D Salient object detection (SOD) aims at distinguishing the most visually distinctive objects or regions in a scene from the given RGB and Depth data. It has a wide range of applications, including video/image segmentation, object recognition, visual tracking, foreground maps evaluation, image retrieval, content-aware image editing, information discovery, photosynthesis, and weakly supervised semantic segmentation. Here, depth information plays an important complementary role in finding salient objects. Online benchmark: http://dpfan.net/d3netbenchmark.
<span style="color:grey; opacity: 0.6">( Image credit: Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks, TNNLS20 )</span>
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Dataset기록된 모델 예시MetricPaperCode
데이터셋
8개논문
S-Measure: 92.1Average MAE: 0.036max E-Measure: 95.4max F-Measure: 92.4
S-Measure: 91.1Average MAE: 0.035
S-Measure: 93.4Average MAE: 0.029max E-Measure: 94.7max F-Measure: 91.3
S-Measure: 90.3Average MAE: 0.046
S-Measure: 93.7Average MAE: 0.023max E-Measure: 96.4max F-Measure: 94.6
S-Measure: 91.1Average MAE: 0.037
S-Measure: 90.3Average MAE: 0.043max E-Measure: 94.4max F-Measure: 90.3
S-Measure: 90.4Average MAE: 0.044
S-Measure: 91.1Average MAE: 0.040max E-Measure: 94.8max F-Measure: 91.3
S-Measure: 91.8Average MAE: 0.032max E-Measure: 95.0max F-Measure: 92.0
S-Measure: 90.2Average MAE: 0.039
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