论文标题
再次使视频对象分割有效
Make One-Shot Video Object Segmentation Efficient Again
论文作者
论文摘要
视频对象分割（VOS）描述了在视频的每个帧中分割一组对象的任务。在半监督设置中，每个对象的第一个掩码在测试时提供。遵循单发原理，微调VOS方法在每个给定的对象掩码上分别训练分割模型。但是，最近，VOS社区认为这类测试时间优化及其对测试运行时的影响是不可行的。为了减轻以前的微调方法的低效率，我们提出有效的一击视频对象分割（E-OSVOS）。与大多数VOS方法相反，E-OSVOS将对象检测任务解开，并仅通过应用Mask R-CNN的修改版本来预测局部分割掩码。一声测试运行时和性能将在没有费力和手工制作的超参数搜索的情况下进行优化。为此，我们学习了测试时间优化的模型初始化和学习率。为了实现最佳的学习行为，我们可以预测神经元水平的个体学习率。此外，我们通过在线适应来解决整个顺序的共同性能降解，通过在先前的掩码预测上不断微调模型，该模型由框架到框架边界框传播支持。 E-Osvos在戴维斯（Davis）2016，戴维斯（Davis）2017和YouTube-VOS上提供了最先进的结果，以实质上减少测试运行时。 代码可在https://github.com/dvl-tum/e-osvos上找到。
Video object segmentation (VOS) describes the task of segmenting a set of objects in each frame of a video. In the semi-supervised setting, the first mask of each object is provided at test time. Following the one-shot principle, fine-tuning VOS methods train a segmentation model separately on each given object mask. However, recently the VOS community has deemed such a test time optimization and its impact on the test runtime as unfeasible. To mitigate the inefficiencies of previous fine-tuning approaches, we present efficient One-Shot Video Object Segmentation (e-OSVOS). In contrast to most VOS approaches, e-OSVOS decouples the object detection task and predicts only local segmentation masks by applying a modified version of Mask R-CNN. The one-shot test runtime and performance are optimized without a laborious and handcrafted hyperparameter search. To this end, we meta learn the model initialization and learning rates for the test time optimization. To achieve optimal learning behavior, we predict individual learning rates at a neuron level. Furthermore, we apply an online adaptation to address the common performance degradation throughout a sequence by continuously fine-tuning the model on previous mask predictions supported by a frame-to-frame bounding box propagation. e-OSVOS provides state-of-the-art results on DAVIS 2016, DAVIS 2017, and YouTube-VOS for one-shot fine-tuning methods while reducing the test runtime substantially. Code is available at https://github.com/dvl-tum/e-osvos.
