whu-nercms at trecvid2021:instance search task
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Abstract (arXiv)
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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References · 8
- 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
- 2019RetinaFace: Single-stage Dense Face Localisation in the Wildcited by 165
- 2021QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Informationcited by 147
- 2019PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detectioncited by 143
- 2021Reformulating HOI Detection as Adaptive Set Predictioncited by 95
- 2021TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Devicecited by 17
- 2018Actor Conditioned Attention Maps for Video Action Detectioncited by 15
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