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论文名：

Predicting Future Instance Segmentation by Forecasting Convolutional Features

作者：

Pauline Luc, Camille Couprie, Yann LeCun, Jakob Verbeek

Abstract

Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and then segmenting these. In this paper we consider the more challenging problem of future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of fixed-sized convolutional features of the Mask R-CNN instance segmentation model. We apply the “detection head” of Mask R-CNN on the predicted features to produce the instance segmentation of future frames. Experiments show that this approach significantly improves over strong baselines based on optical flow and repurposed instance segmentation architectures.

中文摘要

预测未来事件是智能行为的重要先决条件。视频预测已被研究作为实现这一目标的代理任务。最近的工作表明，为了预测未来帧的语义分割，在语义级别进行预测比预测RGB帧然后对这些帧进行分段更有效。在本文中，我们考虑未来实例分割的更具挑战性的问题，其另外分割出单个对象。为了处理每个图像的不同数量的输出标签，我们在Mask R-CNN实例分割模型的固定大小的卷积特征的空间中开发了预测模型。我们将Mask R-CNN的“检测头”应用于预测的特征，以产生未来帧的实例分割。

论文下载链接

http://t.cn/EvKjhve