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%. Code is available athttps://github.com/nercms-mmap/caaf.

Person re-identification (re-ID) is widely applied in surveillance and criminal detection applications. The existing research focus on devising the stand-alone re-ID methods, ignoring their practical application in the multi-person collaboration scenario. To improve the search efficiency, a group of investigators are usually assigned the same task to re-identify a suspect from a shared gallery set. Due to their personalized viewpoints and search feedback operations, different investigators may obtain diverse search results of the same query target. In this case, merging different rankings and generating an improved result is of great importance. To this end, this paper proposes a crowdsourcing-based ranking aggregation to adaptively fuse multiple ranking lists for re-ID problem. The method estimates the reliability of individual investigators, with a specifically designed long tail distribution to fit the top ranking demand, and is feasible for human-machine interaction. Extensive experiments conducted on four dataset-s demonstrate the superiority of the proposed method.

Re-ranking has been proved an effective method to boost the performance of person re-identification. Existing works focus on contextual or graph-based similarity to improve the initial ranking result. The former mainly concentrates on more accurate similarity description but neglects the manifold constraint. While, the later centers on solving similarities with manifold constraint, which acquires several accurate top ranks. In this paper, we propose a novel method which not only takes contextual similarity to generate top ranks accurately but also refines the ranks based on graph-based similarity. Specifically, given initial Euclidean distances between a probe and galleries, we mine contextual and graph-based similarities respectively and then re-rank all galleries with a diffusion procedure under constraints of both similarities. Experiments on two person re-ID datasets demonstrate that our method outperforms state-of-the-art re-ranking approaches in person re-identification.