PUC Chile team at VQA-Med 2021: approaching VQAas a classification task via fine-tuning a pretraine

RL1, Publisher: Working Notes of CLEF, Link>


Ricardo Schilling, Pablo Messina, Denis Parra, H Lobel


This paper describes the submission of the IALab group of the Pontifical Catholic University of Chile to the Medical Domain Visual Question Answering (VQA-Med) task. Our participation was rather simple: we approached the problem as image classification. We took a DenseNet121 with its weights pre-trained in ImageNet and fine-tuned it with the VQA-Med 2020 dataset labels to predict the answer. Different answers were treated as different classes, and the questions were disregarded for simplicity since essentially they all ask for abnormalities. With this very simple approach we ranked 7th among 11 teams, with a test set accuracy of 0.236.

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