Abstract
Multi-instance video object segmentation is to segment specific instances throughout a video sequence in pixel level, given only an annotated first frame. In this paper, we implement an effective fully convolutional networks with U-Net similar structure built on top of OSVOS fine-tuned layer. We use instance isolation to transform this multi-instance segmentation problem into binary labeling problem, and use weighted cross entropy loss and dice coefficient loss as our loss function. Our best model achieves F mean of 0.467 and J mean of 0.424 on DAVIS dataset, which is a comparable performance with the State-of-the-Art approach. But case analysis shows this model can achieve a smoother contour and better instance coverage, meaning it better for recall focused segmentation scenario. We also did experiments on other convolutional neural networks, including Seg-Net, Mask R-CNN, and provide insightful comparison and discussion.
Abstract (translated)
多实例视频对象分割是在一个视频序列中以像素级对特定实例进行分割，只给出一个带注释的第一帧。本文在OSVOS微调层的基础上，实现了一种有效的U网结构的全卷积网络。我们利用实例隔离将多实例分割问题转化为二值标记问题，并以加权交叉熵损失和骰子系数损失作为损失函数。我们的最佳模型在Davis数据集上实现了f平均值0.467和j平均值0.424，这与最先进的方法相当。但案例分析表明，该模型能够获得更平滑的轮廓和更好的实例覆盖率，这意味着它更适合于以回忆为中心的分割场景。我们还对其他卷积神经网络进行了实验，包括SEG网、掩模R-CNN，并进行了深入的比较和讨论。
URL
https://arxiv.org/abs/1905.07826
