See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks (CVPR2019)


We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video frames and incorporate a global co-attention mechanism to improve further the state-of-the-art deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in short-term temporal segments.

The co attention layers in our network provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending co-attention responses into a joint feature space.

We train COSNet with pairs of video frames, which naturally augments training data and allows increased learning capacity. During the segmentation stage, the co-attention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better.

We propose a unified and end-to end trainable framework where different co-attention variants can be derived for mining the rich context within videos. Our extensive experiments over three large benchmarks manifest that COSNet outperforms the current alternatives by a large margin.




  • idea:
    • 作者提出一种co-attention,基于一个视频序列全局角度,来提升UVOS的精度。(确实领先目前的很多模型,davis官网的数据)。以往的一些方法,有通过显著性检测得到所要分割的目标,或者通过有限帧之间计算出的光流信息。COSNet则从整个视频序列中考虑哪个目标是需要分割的。在测试阶段,COSNet会综合所有前面的帧得到的信息,推理出当前帧中哪个目标是显著的同时还是经常出现的。Co-attention模块挖掘了视频帧之间丰富的上下文信息。基于co-attention,作者提出了COSNet(co attention Siamese)来从一个全局视角建模UVOS 。现在可能读者还是不能理解这个全局视角是什么,在method部分会解释。
  • contribution:
    • COSNet采用的训练方式是考虑一个pair,包含相同视频中的任意两帧,所以说极大的增加了数据量,不需要考虑时序关系,依次送入数据,而是可以打乱数据,随机组合。
    • 显示建模帧和帧的联系,不依赖光流
    • 统一的,端到端、可训练的高效网络
  • unsupervised:
    • UVOS中的unsupervised指的是不给定前景目标,通过网络自动判断哪个是前景目标。而非传统意义的label不参与训练过程。


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