Siamese　tracking　methods　have　become　the　focus　of　visual　tracking　in　recent　years.　Advanced　Siamese　trackers　perform　well　on　certain　benchmarks,　but　there　are　still　some　limitations.　First,　most　Siamese　trackers　adopt　the　initial　frame　as　a　single　template,　which　leads　to　underfitting　and　reduces　the　ability　to　predict　instances.　Second,　mainstream　trackers　report　a　rectangular　bounding　box　as　a　prediction,　resulting　in　poor　accuracy　of　non-rigid　objects.　Therefore,　we　propose　the　template　enhancement　and　mask　generation　for　Siamese　tracking.　Given　that　the　essence　of　Siamese　trackers　is　instance　learning,　we　propose　constructing　an　alternative　template　explicitly　to　address　the　underfitting　of　the　instance　space.　Moreover,　in　order　to　improve　the　tracking　accuracy,　we　obtain　the　descriptor　aggregation　to　transform　the　semantic　segmentation　outputs　for　mask　prediction.　Finally,　we　propose　the　SiamEM　through　the　fusion　of　the　above　approaches.　Comprehensive　experiments　show　that　template　enhancement　and　mask　generation　significantly　improve　Siamese　trackers　on　benchmarks.　©　1994-2012　IEEE.