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Efficient Adaptive Image-Language Learning for Visual Question Answering

CVPR Workshop on Transformers for Vision (T4V) (2022)
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We present a novel efficient image-language learning model for multi-task visual question answering tasks which works at a fraction of the computational cost. New compact features are learned adaptively to jointly represent the image and language modalities according to the data. Our method outperforms the state-of-the-art multi-task approaches on SNLI-VE and GQA, and works competitively on VQA2.0. The model is highly efficient using 7-10 fewer GFLOPs and scales well to more than twice the input image size.

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