CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation

Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features the detection, segmentation, tracking of objects they suffer video scenario due several distinct challenges such as motion blur drastic appearance change. To eliminate ambiguities introduced by using features, propose novel comprehensive feature aggregation approach (CompFeat) refine atboth frame-level object-level with temporal spatial context information. The process carefully designed new attention mechanism significantly increases discriminative power learned features. We further improve capability our model through siamese design incorporating both similarities similarities. Experiments conducted on YouTube-VIS dataset validate effectiveness proposed CompFeat.