# Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck Source: [Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:28:17.841Z Authors: Jayesh Songara, Shivam Pande, Shabnam Choudhury, Biplab Banerjee, Rajbabu Velmurugan Published: 2023-06-25T15:09:21.000Z ## Abstract In this research, we deal with the problem of visual question answering (VQA) in remote sensing. While remotely sensed images contain information significant for the task of identification and object detection, they pose a great challenge in their processing because of high dimensionality, volume and redundancy. Furthermore, processing image information jointly with language features adds additional constraints, such as mapping the corresponding image and language features. To handle this problem, we propose a cross attention based approach combined with information maximization. The CNN-LSTM based cross-attention highlights the information in the image and language modalities and establishes a connection between the two, while information maximization learns a low dimensional bottleneck layer, that has all the relevant information required to carry out the VQA task. We evaluate our method on two VQA remote sensing datasets of different resolutions. For the high resolution dataset, we achieve an overall accuracy of 79.11% and 73.87% for the two test sets while for the low resolution dataset, we achieve an overall accuracy of 85.98%. ## 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]()