Analysis on Image Set Visual Question Answering

• URL: http://arxiv.org/abs/2104.00107v1
• Date: Wed, 31 Mar 2021 20:47:32 GMT
• Title: Analysis on Image Set Visual Question Answering
• Authors: Abhinav Khattar, Aviral Joshi, Har Simrat Singh, Pulkit Goel, Rohit Prakash Barnwal
• Abstract summary: We tackle the challenge of Visual Question Answering in multi-image setting. Traditional VQA tasks have focused on a single-image setting where the target answer is generated from a single image. In this report, we work with 4 approaches in a bid to improve the performance on the task.
• Score: 0.3359875577705538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We tackle the challenge of Visual Question Answering in multi-image setting for the ISVQA dataset. Traditional VQA tasks have focused on a single-image setting where the target answer is generated from a single image. Image set VQA, however, comprises of a set of images and requires finding connection between images, relate the objects across images based on these connections and generate a unified answer. In this report, we work with 4 approaches in a bid to improve the performance on the task. We analyse and compare our results with three baseline models - LXMERT, HME-VideoQA and VisualBERT - and show that our approaches can provide a slight improvement over the baselines. In specific, we try to improve on the spatial awareness of the model and help the model identify color using enhanced pre-training, reduce language dependence using adversarial regularization, and improve counting using regression loss and graph based deduplication. We further delve into an in-depth analysis on the language bias in the ISVQA dataset and show how models trained on ISVQA implicitly learn to associate language more strongly with the final answer.

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