Learning to Segment Instances in Videos with Spatial Propagation Network
This work addresses video instance segmentation, a key task in computer vision for applications like autonomous driving and video editing, but it is incremental, building on existing methods with refinements.
The paper tackles instance-level object segmentation in videos by proposing a deep learning framework that uses a generic foreground/background model, fine-tunes for instances, and refines segmentation with a spatial propagation network and consistency filters, achieving competitive results on benchmark datasets.
We propose a deep learning-based framework for instance-level object segmentation. Our method mainly consists of three steps. First, We train a generic model based on ResNet-101 for foreground/background segmentations. Second, based on this generic model, we fine-tune it to learn instance-level models and segment individual objects by using augmented object annotations in first frames of test videos. To distinguish different instances in the same video, we compute a pixel-level score map for each object from these instance-level models. Each score map indicates the objectness likelihood and is only computed within the foreground mask obtained in the first step. To further refine this per frame score map, we learn a spatial propagation network. This network aims to learn how to propagate a coarse segmentation mask spatially based on the pairwise similarities in each frame. In addition, we apply a filter on the refined score map that aims to recognize the best connected region using spatial and temporal consistencies in the video. Finally, we decide the instance-level object segmentation in each video by comparing score maps of different instances.