# On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering Source: [On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:50:06.694Z Authors: Gouthaman KV, Anurag Mittal Published: 2021-08-28T05:51:27.000Z ## Abstract Generalizing beyond the experiences has a significant role in developing practical AI systems. It has been shown that current Visual Question Answering (VQA) models are over-dependent on the language-priors (spurious correlations between question-types and their most frequent answers) from the train set and pose poor performance on Out-of-Distribution (OOD) test sets. This conduct limits their generalizability and restricts them from being utilized in real-world situations. This paper shows that the sequence model architecture used in the question-encoder has a significant role in the generalizability of VQA models. To demonstrate this, we performed a detailed analysis of various existing RNN-based and Transformer-based question-encoders, and along, we proposed a novel Graph attention network (GAT)-based question-encoder. Our study found that a better choice of sequence model in the question-encoder improves the generalizability of VQA models even without using any additional relatively complex bias-mitigation approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()