Structured Query-Based Image Retrieval Using Scene Graphs

被引:31
|
作者
Schroeder, Brigit [1 ]
Tripathi, Subarna [2 ]
机构
[1] Univ Calif Santa Cruz, Santa Cruz, CA 95064 USA
[2] Intel Labs, Santa Clara, CA USA
来源
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2020) | 2020年
关键词
D O I
10.1109/CVPRW50498.2020.00097
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A structured query can capture the complexity of object interactions (e.g. 'woman rides motorcycle') unlike single objects (e.g. 'woman' or 'motorcycle'). Retrieval using structured queries therefore is much more useful than single object retrieval, but a much more challenging problem. In this paper we present a method which uses scene graph embeddings as the basis for an approach to image retrieval. We examine how visual relationships, derived from scene graphs, can be used as structured queries. The visual relationships are directed subgraphs of the scene graph with a subject and object as nodes connected by a predicate relationhship. Notably, we are able to achieve high recall even on low to medium frequency objects found in the long-tailed COCO-Stuff dataset, and find that adding a visual relationship-inspired loss boosts our recall by 10% in the best case.
引用
收藏
页码:680 / 684
页数:5
相关论文
共 50 条
  • [21] Cross-Modal Attention Preservation with Self-Contrastive Learning for Composed Query-Based Image Retrieval
    Li, Shenshen
    Xu, Xing
    Jiang, Xun
    Shen, Fumin
    Sun, Zhe
    Cichocki, Andrzej
    ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2024, 20 (06)
  • [22] Ontology-Based Natural Query Retrieval Using Conceptual Graphs
    Quan, Tho Thanh
    Hui, Siu Cheung
    PRICAI 2008: TRENDS IN ARTIFICIAL INTELLIGENCE, 2008, 5351 : 309 - +
  • [23] Image-to-Image Retrieval by Learning Similarity between Scene Graphs
    Yoon, Sangwoong
    Kang, Woo Young
    Jeon, Sungwook
    Lee, SeongEun
    Han, Changjin
    Park, Jonghun
    Kim, Eun-Sol
    THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 10718 - 10726
  • [24] An efficient image retrieval system with structured query based feature selection and filtering initial level relevant images using range query
    Annrose, J.
    Christopher, C. Seldev
    OPTIK, 2018, 157 : 1053 - 1064
  • [25] IMAGE RETRIEVAL USING NOISY QUERY
    Zhang, Jun
    Ye, Lei
    ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3, 2009, : 866 - 869
  • [26] Content Based Image Retrieval using Query by Approximate Shape
    Deniziak, Stanislaw
    Michno, Tomasz
    PROCEEDINGS OF THE 2016 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2016, 8 : 807 - 816
  • [27] Query-based Policy Literature Retrieval Method Based on Semi-Supervised Framework
    Wei, Moji
    Guo, Yanyan
    Li, Chen
    PROCEEDINGS OF INTERNATIONAL CONFERENCE ON MODELING, NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING, CMNM 2024, 2024, : 28 - 32
  • [28] Disease diagnosis using query-based neural networks
    Chang, RI
    ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS, 2005, 3498 : 767 - 773
  • [29] An Efficient Multi Query System for Content Based Image Retrieval Using Query Replacement
    Vimina, E. R.
    Ramakrishnan, K.
    Nandakumar, Navya
    Jacob, Poulose K.
    2015 16TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), 2015, : 43 - 47
  • [30] Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos
    Zhang, Zhu
    Lin, Zhijie
    Zhao, Zhou
    Xiao, Zhenxin
    PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), 2019, : 655 - 664