You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Siamese Learning Visual Tracking: A Survey 
Chapter Title:  ACKNOWLEDGMENTS 
1. Abstract of Cited Paper (BIB001):  In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-theart tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos. The presented tracker simply matches the initial patch of the target in the first frame with candidates in a new frame and returns the most similar patch by a learned matching function. The strength of the matching function comes from being extensively trained generically, i.e., without any data of the target, using a Siamese deep neural network, which we design for tracking. Once learned, the matching function is used as is, without any adapting, to track previously unseen targets. It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, which only uses the original observation of the target from the first frame, suffices to reach state-of-theart performance. Further, we show the proposed tracker even allows for target re-identification after the target was absent for a complete video shot.

Literature Review:
I thank all reviewers for reading the article and for their valuable comments which improved substan- BIB001 . SINT performs best with IoU/prec. 62.5/84.8 % on OTB-2013 and OPE BIB002 . tially the work. This research has received funding from the EU ARTEMIS Joint Undertaking under grant agreements no. 621429 (EMš) and from the FFG (Austrian Research Promotion Agency) on behalf of BMVIT, The Federal Ministry of Transport, Innovation and Technology. This work was supported by the AIT strategic research programme 2017 Visual Surveillance and Insight.