whu-nercms at trecvid2021:instance search task

Yanrui Niu; Jingyao Yang; Ankang Lu; Baojin Huang; Yue Zhang; Ji Huang; Shishi Wen; Dongshu Xu; Chao Liang; Zhongyuan Wang; Jun Chen

arXiv · 2111.00228

whu-nercms at trecvid2021:instance search task

Yanrui Niu·Jingyao Yang·Ankang Lu·Baojin Huang·Yue Zhang·Ji Huang·Shishi Wen·Dongshu Xu·Chao Liang·Zhongyuan Wang·Jun Chen

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Abstract

We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper. This year we participate in the automatic and interactive tasks of Instance Search (INS). For the automatic task, the retrieval target is divided into two parts, person retrieval, and action retrieval. We adopt a two-stage method including face detection and face recognition for person retrieval and two kinds of action detection methods consisting of three frame-based human-object interaction detection methods and two video-based general action detection methods for action retrieval. After that, the person retrieval results and action retrieval results are fused to initialize the result ranking lists. In addition, we make attempts to use complementary methods to further improve search performance. For interactive tasks, we test two different interaction strategies on the fusion results. We submit 4 runs for automatic and interactive tasks respectively. The introduction of each run is shown in Table 1. The official evaluations show that the proposed strategies rank 1st in both automatic and interactive tracks.

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BibTeX RIS

Yanrui Niu, Jingyao Yang, Ankang Lu, Baojin Huang, Yue Zhang, Ji Huang, Shishi Wen, Dongshu Xu, Chao Liang, Zhongyuan Wang, Jun Chen. 2021-10-30. whu-nercms at trecvid2021:instance search task. https://arxiv.org/abs/2111.00228

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