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of 59
pro vyhledávání: '"Li, Shuiwang"'
Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robust algorithms. While significant progress has been made in tracking gen
Externí odkaz:
http://arxiv.org/abs/2408.13877
Autor:
Zhong, Pengzhi, Guo, Xiaoyu, Huang, Defeng, Peng, Xiaojun, Li, Yian, Zhao, Qijun, Li, Shuiwang
In recent years, the field of visual tracking has made significant progress with the application of large-scale training datasets. These datasets have supported the development of sophisticated algorithms, enhancing the accuracy and stability of visu
Externí odkaz:
http://arxiv.org/abs/2408.11463
Object detection has greatly improved over the past decade thanks to advances in deep learning and large-scale datasets. However, detecting objects reflected in surfaces remains an underexplored area. Reflective surfaces are ubiquitous in daily life,
Externí odkaz:
http://arxiv.org/abs/2407.05575
Recently, the surge in the adoption of single-stream architectures utilizing pre-trained ViT backbones represents a promising advancement in the field of generic visual tracking. By integrating feature extraction and fusion into a cohesive framework,
Externí odkaz:
http://arxiv.org/abs/2407.05383
Visual tracking has advanced significantly in recent years, mainly due to the availability of large-scale training datasets. These datasets have enabled the development of numerous algorithms that can track objects with high accuracy and robustness.H
Externí odkaz:
http://arxiv.org/abs/2407.05235
Empowered by transformer-based models, visual tracking has advanced significantly. However, the slow speed of current trackers limits their applicability on devices with constrained computational resources. To address this challenge, we introduce ABT
Externí odkaz:
http://arxiv.org/abs/2406.08037
Tracking transforming objects holds significant importance in various fields due to the dynamic nature of many real-world scenarios. By enabling systems accurately represent transforming objects over time, tracking transforming objects facilitates ad
Externí odkaz:
http://arxiv.org/abs/2404.18143
Maintaining high efficiency and high precision are two fundamental challenges in UAV tracking due to the constraints of computing resources, battery capacity, and UAV maximum load. Discriminative correlation filters (DCF)-based trackers can yield hig
Externí odkaz:
http://arxiv.org/abs/2308.11450
Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking
Efficiency has been a critical problem in UAV tracking due to limitations in computation resources, battery capacity, and unmanned aerial vehicle maximum load. Although discriminative correlation filters (DCF)-based trackers prevail in this field for
Externí odkaz:
http://arxiv.org/abs/2308.10262
With more and more large-scale datasets available for training, visual tracking has made great progress in recent years. However, current research in the field mainly focuses on tracking generic objects. In this paper, we present TSFMO, a benchmark f
Externí odkaz:
http://arxiv.org/abs/2209.04284