首页|Findings on Computational Intelligence Discussed by Investigators at Harbin Engi neering University (Vtst: Efficient Visual Tracking With a Stereoscopic Transfor mer)
Findings on Computational Intelligence Discussed by Investigators at Harbin Engi neering University (Vtst: Efficient Visual Tracking With a Stereoscopic Transfor mer)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing - Computational Intelligence have been published. According to news reportin g from Harbin, People's Republic of China, by NewsRx journalists, research state d, "Although Siamese trackers have become increasingly prevalent in the visual t racking domain, they are easily interfered by semantic distractors in complex en vironments, which results in the underutilization of feature information. Especi ally when multiple disturbances work together, the performance of many trackers often suffers severe degradation." Financial support for this research came from Natural Science Foundation of Heil ongjiang Province. The news correspondents obtained a quote from the research from Harbin Engineeri ng University, "To solve the above problem, this paper presents a robust Stereos copic Transformer network for improving tracking performance. Using a hybrid att ention mechanism, our method is composed of a channel feature awareness network (CFAN), a global channel attention network (GCAN), and a multi-level feature enh ancement unit (MFEU). Concretely, CFAN focuses on specific channel information, while highlighting the contained target features and weakening the semantic dist ractor features. As an intermediate hub, GCAN is mainly responsible for establis hing the global feature dependencies between the search region and the template, while selecting the concerned channel features to improve the distinguishing ab ility of the model. In particular, MFEU is used to enhance multi-level feature i nformation to facilitate feature representation learning for our method. Finally , a Transformer-based Siamese tracker (named VTST) is proposed to present an eff icient tracking representation, which can gain advantages over a variety of chal lenging attributes."
HarbinPeople's Republic of ChinaAsiaComputational IntelligenceMachine LearningHarbin Engineering University
NETL
NSTL
