paper-with-me

Papers

whu-nercms at trecvid2021:instance search task

2021-10-30 · Yanrui Niu, Jingyao Yang, Ankang Lu, Baojin Huang, Yue Zhang, Ji Huang, Shishi Wen, Dongshu Xu, Chao Liang, Zhongyuan Wang, Jun Chen

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.

📄 PDF Abstract BibTeX arXiv:2111.00228

Code (0)

등록된 구현이 없습니다.

Tasks

Action DetectionFace DetectionFace RecognitionHuman-Object Interaction DetectionInstance SearchPerson RetrievalRetrieval

Methods 이 논문이 사용한 방법론

Test 설명 없음

Similar Papers 제목 키워드 기반

Confidence-Aware Active Feedback for Interactive Instance Search

2021-10-23 · Yue Zhang, Chao Liang, Longxiang Jiang

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…

Active LearningInstance SearchRe-Ranking

TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval

2020-09-21 · George Awad, Asad A. Butt, Keith Curtis, Yooyoung Lee 외

The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and ret…

Action DetectionActivity DetectionAd-hoc video searchInstance Search+3

TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains

2021-04-27 · George Awad, Asad A. Butt, Keith Curtis, Jonathan Fiscus 외

The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of informa…

Ad-hoc video searchInstance SearchRetrievalVideo Retrieval+1

Faster R-CNN Features for Instance Search

2016-04-29 · Amaia Salvador, Xavier Giro-i-Nieto, Ferran Marques, Shin'ichi Satoh

Image representations derived from pre-trained Convolutional Neural Networks (CNNs) have become the new state of the art in computer vision tasks such as instance retrieval. This work explores the suitability for instanc…

Instance Searchobject-detectionObject DetectionRegion Proposal+2

An overview on the evaluated video retrieval tasks at TRECVID 2022

2023-06-22 · George Awad, Keith Curtis, Asad Butt, Jonathan Fiscus 외

The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of informa…

Ad-hoc video searchRetrievalVideo RetrievalVideo Understanding