Surgical-VQLA++: Adversarial contrastive learning for calibrated robust visual question-localized answering in robotic surgery-Reference-Cited by-同舟云学术

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Surgical-VQLA++: Adversarial contrastive learning for calibrated robust visual question-localized answering in robotic surgery

Published:2025-01 Issue: Volume:113 Page:102602
ISSN:1566-2535
Container-title:Information Fusion
language:en
Short-container-title:Information Fusion

Author:

Bai Long^ORCID,Wang Guankun^ORCID,Islam Mobarakol^ORCID,Seenivasan Lalithkumar^ORCID,Wang An^ORCID,Ren Hongliang^ORCID

Funder

University College London

The Chinese University of Hong Kong

Publisher

Elsevier BV

Reference75 articles.

1. Surgical-VQA: Visual question answering in surgical scenes using transformer;Seenivasan,2022

2. MedFuseNet: An attention-based multimodal deep learning model for visual question answering in the medical domain;Sharma;Sci. Rep.,2021

3. Surgical-VQLA: Transformer with gated vision-language embedding for visual question localized-answering in robotic surgery;Bai,2023

4. Visualbert: A simple and performant baseline for vision and language;Li,2019

5. Multimodal research in vision and language: A review of current and emerging trends;Uppal;Inf. Fusion,2022

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