Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning

This paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest. The problem is formulated as pixel-wise retrieval in a learned embedding space: we embed pixels of the same object instance into the vicinity of each other, using a fully convolutional network trained by a modified triplet loss as the embedding model... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Semi-Supervised Video Object Segmentation DAVIS 2016 PML Jaccard (Mean) 75.5 # 25
Jaccard (Recall) 89.6 # 19
Jaccard (Decay) 8.5 # 17
F-measure (Mean) 79.3 # 21
F-measure (Recall) 93.4 # 8
F-measure (Decay) 7.8 # 10
J&F 77.4 # 24

Methods used in the Paper


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