FeatInter: Exploring fine-grained object features for video-text retrieval-Reference-Cited by-同舟云学术

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FeatInter: Exploring fine-grained object features for video-text retrieval

Published:2022-07 Issue: Volume:496 Page:178-191
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Liu Baolong,Zheng Qi,Wang Yabing,Zhang Minsong,Dong Jianfeng,Wang Xun

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference82 articles.

1. Interventional video relation detection;Li,2021

2. Visual relation grounding in videos;Xiao,2020

3. Deep neighborhood component analysis for visual similarity modeling;Liu;ACM Transactions on Intelligent Systems and Technology (TIST),2020

4. Annotating objects and relations in user-generated videos;Shang,2019

5. Participation-contributed temporal dynamic model for group activity recognition;Yan,2018

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2. Deep Learning Models for Fast Retrieval and Extraction of French Speech Vocabulary Applications;Computational Intelligence and Neuroscience;2022-07-08

3. OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval;2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2022-06

4. Event-Triggered Cluster Consensus of Multi-Agent Systems via a Modified Genetic Algorithm;IEEE Transactions on Neural Networks and Learning Systems;2022

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