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STMTrack: Template-free Visual Tracking with Space-time Memory Networks [article]

Zhihong Fu, Qingjie Liu, Zehua Fu, Yunhong Wang
2021 arXiv   pre-print
In this paper, we propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to  ...  Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance, hindering them from real-time  ...  Conclusions This work proposes a novel tracking framework based on space-time memory networks.  ... 
arXiv:2104.00324v2 fatcat:b62e5caf7fgq5ngumdnfztl5ky

Ranking-Based Siamese Visual Tracking [article]

Feng Tang, Qiang Ling
2022 arXiv   pre-print
Current Siamese-based trackers mainly formulate the visual tracking into two independent subtasks, including classification and localization.  ...  Specifically, the proposed two ranking losses are compatible with most Siamese trackers and incur no additional computation for inference.  ...  Related Work Siamese visual tracking Recently, SiamFC [1] formulates the visual tracking task as a general similarity computation problem between the target template and the search region, which learns  ... 
arXiv:2205.11761v1 fatcat:rnq57xv4t5egta5kbrdpbkib3i

Siamese Attribute-missing Graph Auto-encoder [article]

Wenxuan Tu, Sihang Zhou, Yue Liu, Xinwang Liu
2021 arXiv   pre-print
literature: 1) isolates the learning of attribute and structure embedding thus fails to take full advantages of the two types of information; 2) imposes too strict distribution assumption on the latent space  ...  First, we entangle the attribute embedding and structure embedding by introducing a siamese network structure to share the parameters learned by both processes, which allows the network training to benefit  ...  STMTrack: 2475–2487. Template-Free Visual Tracking With Space-Time Memory Peng, Z.; Huang, W.; Luo, M.; Zheng, Q.; Rong, Y.; Xu, Networks.  ... 
arXiv:2112.04842v1 fatcat:2hcdamwg5zf2diopgvp2whjdhi