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Articles

Computer Science > Computer Vision and Pattern Recognition

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By Bineng Zhong, Qihua Liang, Yaozong Zheng
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2507.16191] Explicit Context Reasoning with Supervision for Visual Tracking

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By Bineng Zhong, Haiying Xia, Yufei Tan, Xiantao Hu
| arXiv
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Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism

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By Jun Zhang, Pengfei Wang, Bineng Zhong, Jixiang Du
| PLOS
To achieve effective visual tracking, a robust feature representation composed of two separate components (i.e., feature learning and selection) for an object is one of the key issues. Typically, a common assumption used in visual tracking is that the raw video sequences are clear, while real-world data is with significant noise and irrelevant patterns. Consequently, the learned features may be not all relevant and noisy.
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