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Instance-level video segmentation requires a solid integration of spatial andtemporal information. However, current methods rely mostly on domain-specificinformation (online learning) to produce accurate instance-level segmentations.We propose a novel approach that relies exclusively on the integration ofgeneric spatio-temporal attention cues. Our strategy, named Multi-AttentionInstance Network (MAIN), overcomes challenging segmentation scenarios overarbitrary videos without modelling sequence- or instance-specific knowledge. Wedesign MAIN to segment multiple instances in a single forward pass, andoptimize it with a novel loss function that favors class agnostic predictionsand assigns instance-specific penalties. We achieve state-of-the-artperformance on the challenging Youtube-VOS dataset and benchmark, improving theunseen Jaccard and F-Metric by 6.8% and 12.7% respectively, while operating atreal-time (30.3 FPS).

MAIN: Multi-Attention Instance Network for Video Segmentation