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.

EVA-VOS 是一种视频目标分割的人机协作注释框架，通过引入代理模型进行逐帧帧选择和注释类型预测，可以实现高准确度且比传统视频注释方法快 3.5 倍的对象掩模生成与注释时间降低。