Learning a data-driven spatio-temporal semantic representation of the objects is the key to coherent and consistent labelling in video. This paper proposes to achieve semantic video object segmentation by learning a data-driven representation which captures the synergy of multiple instances from continuous frames. To prune the noisy detections, we exploit the rich information among multiple instances and select the discriminative and representative subset. This selection process is formulated as a facility location problem solved by maximising a submodular function. Our method retrieves the longer term contextual dependencies which underpins a robust semantic video object segmentation algorithm. We present extensive experiments on a challenging dataset that demonstrate the superior performance of our approach compared with the state-of-the-art methods.

通过学习数据驱动的时空语义表示，提出了一种实现语义视频目标分割的方法，通过捕捉连续帧中多个实例之间的协同作用，并选择具有区分性和代表性的子集来剪枝嘈杂的检测，从而解决设施定位问题，实现了更长时间的上下文依赖，进而实现了鲁棒的语义视频目标分割算法，通过在具有挑战性的数据集上进行广泛实验，证明了我们方法相比最先进方法的卓越性能。

基于子模块的语义物体分割视频对象候选选择