# VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering Source: [VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2026-03-05T05:00:11.275Z Authors: Ekta Sood, Fabian K\\"ogel, Florian Strohm, Prajit Dhar, Andreas Bulling Published: 2021-09-27T15:06:10.000Z ## Abstract We present VQA-MHUG - a novel 49-participant dataset of multimodal human gaze on both images and questions during visual question answering (VQA) collected using a high-speed eye tracker. We use our dataset to analyze the similarity between human and neural attentive strategies learned by five state-of-the-art VQA models: Modular Co-Attention Network (MCAN) with either grid or region features, Pythia, Bilinear Attention Network (BAN), and the Multimodal Factorized Bilinear Pooling Network (MFB). While prior work has focused on studying the image modality, our analyses show - for the first time - that for all models, higher correlation with human attention on text is a significant predictor of VQA performance. This finding points at a potential for improving VQA performance and, at the same time, calls for further research on neural text attention mechanisms and their integration into architectures for vision and language tasks, including but potentially also beyond VQA. ## 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]()