Learning the What and How of Annotation in Video Object Segmentation
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)(2023)
摘要
Video Object Segmentation (VOS) is crucial for several applications, from
video editing to video data generation. Training a VOS model requires an
abundance of manually labeled training videos. The de-facto traditional way of
annotating objects requires humans to draw detailed segmentation masks on the
target objects at each video frame. This annotation process, however, is
tedious and time-consuming. To reduce this annotation cost, in this paper, we
propose EVA-VOS, a human-in-the-loop annotation framework for video object
segmentation. Unlike the traditional approach, we introduce an agent that
predicts iteratively both which frame ("What") to annotate and which annotation
type ("How") to use. Then, the annotator annotates only the selected frame that
is used to update a VOS module, leading to significant gains in annotation
time. We conduct experiments on the MOSE and the DAVIS datasets and we show
that: (a) EVA-VOS leads to masks with accuracy close to the human agreement
3.5x faster than the standard way of annotating videos; (b) our frame selection
achieves state-of-the-art performance; (c) EVA-VOS yields significant
performance gains in terms of annotation time compared to all other methods and
baselines.
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关键词
Algorithms,Video recognition and understanding,Algorithms,Datasets and evaluations
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