See More, Know More: Unsupervised Video Object Segmentation with CoAttention Siamese Networks. We introduce a novel network, called COattention 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 coattention mechanism to improve further the stateoftheart deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in shortterm temporal segments. The coattention layers in our network provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending coattention 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 coattention 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 endtoend trainable framework where different coattention 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.

ZeroShot Video Object Segmentation via Attentive Graph Neural Networks This work proposes a novel attentive graph neural network (AGNN) for zeroshot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative information fusion over video graphs. Specifically, AGNN builds a fully connected graph to efficiently represent frames as nodes, and relations between arbitrary frame pairs as edges. The underlying pairwise relations are described by a differentiable attention mechanism. Through parametric message passing, AGNN is able to efficiently capture and mine much richer and higherorder relations between video frames, thus enabling a more complete understanding of video content and more accurate foreground estimation. Experimental results on three video segmentation datasets show that AGNN sets a new stateoftheart in each case. To further demonstrate the generalizability of our framework, we extend AGNN to an additional task: image object cosegmentation (IOCS). We perform experiments on two famous IOCS datasets and observe again the superiority of our AGNN model. The extensive experiments verify that AGNN is able to learn the underlying semanticappearance relationships among video frames or related images, and discover the common objects.