Online relevance feedback (RF) is widely utilized in instance search (INS) tasks to further refine imperfect ranking results, but it often has low interaction efficiency. The active learning (AL) technique addresses this problem by selecting valuable feedback candidates. However, mainstream AL methods require an initial labeled set for a cold start and are often computationally complex to solve. Therefore, they cannot fully satisfy the requirements for online RF in interactive INS tasks. To address this issue, we propose a confidence-aware active feedback method (CAAF) that is specifically designed for online RF in interactive INS tasks. Inspired by the explicit difficulty modeling scheme in self-paced learning, CAAF utilizes a pairwise manifold ranking loss to evaluate the ranking confidence of each unlabeled sample. The ranking confidence improves not only the interaction efficiency by indicating valuable feedback candidates but also the ranking quality by modulating the diffusion weights in manifold ranking. In addition, we design two acceleration strategies, an approximate optimization scheme and a top-K search scheme, to reduce the computational complexity of CAAF. Extensive experiments on both image INS tasks and video INS tasks searching for buildings, landscapes, persons, and human behaviors demonstrate the effectiveness of the proposed method. Notably, in the real-world, large-scale video INS task of NIST TRECVID 2021, CAAF uses 25% fewer feedback samples to achieve a performance that is nearly equivalent to the champion solution. Moreover, with the same number of feedback samples, CAAF's mAP is 51.9%, significantly surpassing the champion solution by 5.9%.
翻译:在线相关反馈( RF) 被广泛用于实例搜索( INS) 任务, 以进一步完善不完善的排名结果, 但它往往具有低互动效率。 积极学习( AL) 技术通过选择有价值的反馈候选人来解决这个问题。 然而, 主流AL 方法需要为寒冷的开始设置初始标签, 并且往往在计算上复杂才能加以解决。 因此, 它们无法在互动的 INS 任务中完全满足对在线RF的要求。 为了解决这个问题, 我们建议了一种有自信的积极反馈方法( CAAAAF), 专门为在线IMS 任务设计了一种有自信的积极反馈方法( CAAAF ) 。 受自制学习中明显困难的模型方案启发, CAAF 使用双对齐的多重排序损失来评估每个未标样本的排名信心。 排名信任不仅提高了互动效率, 并且通过显示有价值的反馈候选人, 并且通过调控的传播量分数。 我们设计了两种加速战略, 大致优化计划和最高K搜索计划, 降低 CAAFAF 的计算复杂性。 在图像上, IM IS 的图像任务中, ISAFAFAF 几乎 25 的大幅 实现 20 的大规模 的进度 的进度 的进度法 。