FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation

CVPR 2019 Paul VoigtlaenderYuning ChaiFlorian SchroffHartwig AdamBastian LeibeLiang-Chieh Chen

Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use. In this work, we propose FEELVOS as a simple and fast method which does not rely on fine-tuning... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Video Object Segmentation DAVIS-2017 FEELVOS mIoU 81.1 # 1
Video Object Segmentation YouTube FEELVOS mIoU 82.1 # 1