发表机构
The Hong Kong Polytechnic University; The University of Hong Kong（香港理工大学; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对视觉跟踪中拒绝机制误弃正确候选的问题，提出P-SRM方法，利用空间响应、历史状态和决策边界选择性恢复可靠预测，在多个数据集上提升跟踪性能。
AI 中文摘要
许多视觉跟踪方法使用拒绝机制来抑制不可靠的预测。然而，这些机制也可能拒绝正确定位的候选对象，导致有用信息未被利用。我们研究如何识别并恢复这些候选对象，同时保留原生接受的输出和候选坐标。为此，我们提出P-SRM（拒绝后选择性恢复方法），该方法结合空间响应、过去接受状态和原生决策边界来重新评估候选对象，并选择性地恢复可靠的预测。我们在六个跟踪器和四个数据集上评估了P-SRM，涵盖类别特定、点和通用对象跟踪。在所有九种配置中，P-SRM改善了被拒绝候选的排名和整体跟踪性能。这些结果表明，拒绝后验证可以识别并恢复被原生拒绝丢弃的有用预测，证明了重用被拒绝信息的价值。项目仓库：此https URL。
英文摘要
Many visual tracking methods use rejection mechanisms to suppress unreliable predictions. However, these mechanisms can also reject correctly localized candidates, leaving useful information unused. We investigate how to identify and recover these candidates while preserving native accepted outputs and candidate coordinates. To this end, we propose P-SRM (Post-rejection Selective Recovery Method), which combines spatial responses, past accepted states, and native decision margins to reassess candidates and selectively restore reliable predictions. We evaluate P-SRM on six trackers and four datasets spanning category-specific, point, and generic object tracking. Across all nine configurations, P-SRM improves rejected-candidate ranking and overall tracking performance. These results show that post-rejection verification can identify and recover useful predictions discarded by native rejection, demonstrating the value of reusing rejected information. Project repository: https://github.com/PalestyHR/P-SRM.
Comments5 pages, 2 figures, 3 tables
