Video Semantic Salient Instance Segmentation: Benchmark Dataset and Baseline
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This paper pushes the envelope on salient regions in a video to decompose them into semantically meaningful components, semantic salient instances. To address this video semantic salient instance segmentation, we construct a new dataset, Semantic Salient Instance Video (SESIV) dataset. Our SESIV dataset consists of 84 high-quality video sequences with pixel-wisely per-frame ground-truth labels annotated for different segmentation tasks. We also provide a baseline for this problem, called Fork-Join Strategy (FJS). FJS is a two-stream network leveraging advantages of two different segmentation tasks, i.e., semantic instance segmentation and salient object segmentation. In FJS, we introduce a sequential fusion that combines the outputs of the two streams to have non-overlapping instances one by one. We also introduce a recurrent instance propagation to refine the shapes and semantic meanings of instances, and an identity tracking to maintain both the identity and the semantic meaning of an instance over the entire video. Experimental results demonstrated the effectiveness of our proposed FJS.
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