Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
arXiv:2104.10386
Abstract
We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less effort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://github.com/yuk6heo/GIS-RAmap.
arXiv commentaccepted to CVPR2021 (oral)
References in corpus (6)
- YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
- The 2018 DAVIS Challenge on Video Object Segmentation
- Motion-Attentive Transition for Zero-Shot Video Object Segmentation
- FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos
- Interactive Video Object Segmentation in the Wild
- Anchor Diffusion for Unsupervised Video Object Segmentation