论文标题
基于比例不一致的促进视频对象细分
Boosting Video Object Segmentation based on Scale Inconsistency
论文作者
论文摘要
我们提出了一个改进框架，以提高预训练的半监督视频对象分割（VOS）模型的性能。我们的工作是基于量表不一致的，这是由于现有VOS模型从不同尺寸的输入帧产生不一致的预测的观察到的。我们将量表不一致作为线索来设计一个像素级的注意模块，该模块汇总了来自不同大小输入的预测的优势。量表不一致还用于根据根据不确定性估计测量的像素级方差正规化训练。我们进一步提出了一个为测试时间优化而定制的自制在线改编，该适应基于规模的不一致而在没有地面真相面具的情况下引导预测。 Davis 16和Davis 17数据集的实验表明，我们的框架可以通常应用于各种VOS模型并提高其性能。
We present a refinement framework to boost the performance of pre-trained semi-supervised video object segmentation (VOS) models. Our work is based on scale inconsistency, which is motivated by the observation that existing VOS models generate inconsistent predictions from input frames with different sizes. We use the scale inconsistency as a clue to devise a pixel-level attention module that aggregates the advantages of the predictions from different-size inputs. The scale inconsistency is also used to regularize the training based on a pixel-level variance measured by an uncertainty estimation. We further present a self-supervised online adaptation, tailored for test-time optimization, that bootstraps the predictions without ground-truth masks based on the scale inconsistency. Experiments on DAVIS 16 and DAVIS 17 datasets show that our framework can be generically applied to various VOS models and improve their performance.
