Unsupervised Video Object Segmentation with Motion-based Bilateral Networks

In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
BENCHMARK
Video Object Segmentation DAVIS 2016 MBNM Average MAE 0.031 # 2
Video Salient Object Detection DAVIS-2016 MBNM S-Measure 0.887 # 2
MAX E-MEASURE 0.966 # 1
MAX F-MEASURE 0.862 # 1
AVERAGE MAE 0.031 # 6
Video Salient Object Detection DAVSOD-easy35 MBNM S-Measure 0.646 # 4
max F-Measure 0.506 # 4
max E-Measure 0.694 # 4
Average MAE 0.109 # 3
Video Salient Object Detection DAVSOD-Normal25 MBNM S-Measure 0.597 # 4
max E-measure 0.665 # 4
Average MAE 0.127 # 3
Video Salient Object Detection FBMS-59 MBNM S-Measure 0.857 # 3
AVERAGE MAE 0.047 # 2
MAX E-MEASURE 0.892 # 2
MAX F-MEASURE 0.816 # 4
Video Salient Object Detection MCL MBNM S-Measure 0.755 # 3
MAX E-MEASURE 0.858 # 3
MAX F-MEASURE 0.698 # 2
AVERAGE MAE 0.119 # 3
Video Salient Object Detection SegTrack v2 MBNM S-Measure 0.809 # 3
MAX F-MEASURE 0.716 # 2
AVERAGE MAE 0.026 # 5
max E-measure 0.878 # 3
Video Salient Object Detection UVSD MBNM S-Measure 0.698 # 4
max E-measure 0.776 # 4
Average MAE 0.079 # 4
Video Salient Object Detection ViSal MBNM S-Measure 0.857 # 4
max E-measure 0.892 # 3
Average MAE 0.047 # 4
Video Salient Object Detection VOS-T MBNM S-Measure 0.742 # 4
max E-measure 0.797 # 4
Average MAE 0.099 # 5

Methods used in the Paper


METHOD TYPE
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