论文标题
复发视频对象细分的课程学习
Curriculum Learning for Recurrent Video Object Segmentation
论文作者
论文摘要
视频对象细分可以理解为一项序列到序列任务，可以从课程学习策略中受益，从而更好，更快地培训深度神经网络。这项工作探讨了不同的时间表采样和框架跳过变化，以显着提高经常性体系结构的性能。我们在Kitti-Mots挑战的汽车课程中的结果表明，令人惊讶的是，相反的时间表采样比经典前锋更好。同样，在训练过程中逐渐跳过框架是有益的，但是只有在用地面真理掩盖而不是预测的训练时。源代码和训练有素的模型可在http://imatge-upc.github.io/rvos-mots/上找到。
Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural networks. This work explores different schedule sampling and frame skipping variations to significantly improve the performance of a recurrent architecture. Our results on the car class of the KITTI-MOTS challenge indicate that, surprisingly, an inverse schedule sampling is a better option than a classic forward one. Also, that a progressive skipping of frames during training is beneficial, but only when training with the ground truth masks instead of the predicted ones. Source code and trained models are available at http://imatge-upc.github.io/rvos-mots/.
