research-article

Video Referring Expression Comprehension via Transformer with Content-conditioned Query

Authors:

Yuexian ZouAuthors Info & Claims

MMIR '23: Proceedings of the 1st International Workshop on Deep Multimodal Learning for Information Retrieval

Pages 39 - 48

https://doi.org/10.1145/3606040.3617439

Published: 29 October 2023 Publication History

Abstract

Video Referring Expression Comprehension (REC) aims to localize a target object in videos based on the queried natural language. Recent improvements in video REC have been made using Transformer-based methods with learnable queries. However, we contend that this naive query design is not ideal given the open-world nature of video REC brought by text supervision. With numerous potential semantic categories, relying on only a few slowupdated queries is insufficient to characterize them. Our solution to this problem is to create dynamic queries that are conditioned on both the input video and language to model the diverse objects referred to. Specifically, we place a fixed number of learnable bounding boxes throughout the frame and use corresponding region features to provide prior information. Also, we noticed that current query features overlook the importance of cross-modal alignment. To address this, we align specific phrases in the sentence with semantically relevant visual areas, annotating them in existing video datasets (VID-Sentence and VidSTG). By incorporating these two designs, our proposed model (called ConFormer) outperforms other models on widely benchmarked datasets. For example, in the testing split of VID-Sentence dataset, ConFormer achieves 8.75% absolute improvement on [email protected] compared to the previous state-of-the-art model.

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Cited By

Lin JChen JPeng KHe XLi ZStiefelhagen RYang K(2024)EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous DrivingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.343764525:11(18964-18977)Online publication date: Nov-2024
https://doi.org/10.1109/TITS.2024.3437645
Hang RXu SLiu Q(2024)A Regionally Indicated Visual Grounding Network for Remote Sensing ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.349084762(1-11)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3490847

Index Terms

Video Referring Expression Comprehension via Transformer with Content-conditioned Query
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Visual content-based indexing and retrieval

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cover image ACM Conferences

MMIR '23: Proceedings of the 1st International Workshop on Deep Multimodal Learning for Information Retrieval

November 2023

75 pages

ISBN:9798400702716

DOI:10.1145/3606040

General Chairs:
Wei Ji
National University of Singapore, Singapore
,
Yinwei Wei
Monash University, Australia
,
Zhedong Zheng
National University of Singapore, Singapore
,
Hao Fei
National University of Singapore, Singapore
,
Tat-seng Chua
National University of Singapore, Singapore

Copyright © 2023 ACM.

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Published: 29 October 2023

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Cited By

Lin JChen JPeng KHe XLi ZStiefelhagen RYang K(2024)EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous DrivingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.343764525:11(18964-18977)Online publication date: Nov-2024
https://doi.org/10.1109/TITS.2024.3437645
Hang RXu SLiu Q(2024)A Regionally Indicated Visual Grounding Network for Remote Sensing ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.349084762(1-11)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3490847

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