"Commands for autonomous vehicles by progressively stacking visual-linguistic representations" by Hang Dai

Article

Commands for autonomous vehicles by progressively stacking visual-linguistic representations

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Hang Dai, Mohamed Bin Zayed University of Artificial Intelligence
Shujie Luo, College of Information Science and Electronic Engineering, Zhejiang University
Yong Ding, College of Information Science and Electronic Engineering, Zhejiang University
Ling Shao, Mohamed Bin Zayed University of Artificial Intelligence & Inception Institute of Artificial Intelligence

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Document Type

Conference Proceeding

Abstract

In this work, we focus on the object referral problem in the autonomous driving setting. We use a stacked visual-linguistic BERT model to learn a generic visual-linguistic representation. Each element of the input is either a word or a region of interest from the input image. To train the deep model efficiently, we use a stacking algorithm to transfer knowledge from a shallow BERT model to a deep BERT model.

DOI

10.1007/978-3-030-66096-3_2

Publication Date

1-3-2021

Keywords

Bidirectional Encoder Representations from Transformers (BERT),
image classification,
natural language processing

Disciplines

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Citation Information

H. Dai, S. Luo, Y. Ding and L. Shao, "Commands for autonomous vehicles by progressively stacking visual-linguistic representations", in Computer Vision – ECCV 2020 Workshops, ECCV 2020, (Lecture Notes in Computer Science, v. 12536), pp. 27-32, 2020. Available: 10.1007/978-3-030-66096-3_2