Related papers: Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving

Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving

URL: http://arxiv.org/abs/2307.09329v2
Date: Fri, 28 Jul 2023 09:50:23 GMT
Title: Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving
Authors: Kaavya Rekanar, Ciar\'an Eising, Ganesh Sistu, Martin Hayes
Abstract summary: This paper presents a preliminary analysis of three popular Visual Question Answering (VQA) models, namely ViLBERT, ViLT, and LXMERT. The performance of these models is evaluated by comparing the similarity of responses to reference answers provided by computer vision experts.
Score: 2.9552300389898094
License: http://creativecommons.org/licenses/by/4.0/
Abstract: This short paper presents a preliminary analysis of three popular Visual Question Answering (VQA) models, namely ViLBERT, ViLT, and LXMERT, in the context of answering questions relating to driving scenarios. The performance of these models is evaluated by comparing the similarity of responses to reference answers provided by computer vision experts. Model selection is predicated on the analysis of transformer utilization in multimodal architectures. The results indicate that models incorporating cross-modal attention and late fusion techniques exhibit promising potential for generating improved answers within a driving perspective. This initial analysis serves as a launchpad for a forthcoming comprehensive comparative study involving nine VQA models and sets the scene for further investigations into the effectiveness of VQA model queries in self-driving scenarios. Supplementary material is available at https://github.com/KaavyaRekanar/Towards-a-performance-analysis-on-pre-trained-VQA-models-for-autono mous-driving.