Confidence-Aware Active Feedback for Efficient Instance Search

Relevance feedback is widely used in instance search (INS) tasks to further refine imperfect ranking results, but it often comes with low interaction efficiency. Active learning (AL) technique has achieved great success in improving annotation efficiency in classification tasks. However, considering irrelevant samples' diversity and class imbalance in INS tasks, existing AL methods cannot always select the most suitable feedback candidates for INS problems. In addition, they are often too computationally complex to be applied in interactive INS scenario. To address the above problems, we propose a confidence-aware active feedback (CAAF) method that can efficiently select the most valuable feedback candidates to improve the re-ranking performance. Specifically, inspired by the explicit sample difficulty modeling in self-paced learning, we utilize a pairwise manifold ranking loss to evaluate the ranking confidence of each unlabeled sample, and formulate the INS process as a confidence-weighted manifold ranking problem. Furthermore, we introduce an approximate optimization scheme to simplify the solution from QP problems with constraints to closed-form expressions, and selects only the top-K samples in the initial ranking list for INS, so that CAAF is able to handle large-scale INS tasks in a short period of time. Extensive experiments on both image and video INS tasks demonstrate the effectiveness of the proposed CAAF method. In particular, CAAF outperforms the first-place record in the public large-scale video INS evaluation of TRECVID 2021.