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Recent progress on visual question answering has explored the merits of grid features for vision language tasks. Meanwhile, transformer-based models have shown.

We apply the GA module and AA module to our transformer based image captioning model, Relationship-Sensitive Transformer (RSTNet). For each attention mod-ule of transformer encoder, the relative geometry informa-tion of grid features is incorporated to calculate a more ac-curate attention distribution.

Run python test_online.py to generate required files and evaluate the performance of rstnet on the official test server of MS COCO dataset.

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Run python test_offline.py to evaluate the performance of rstnet on the Karpathy test split of MS COCO dataset. Run python test_online.py to generate required files and evaluate the performance of rstnet on the official test server of MS COCO dataset.

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To prove the generality of our proposals, we apply the two modules to the vanilla transformer model to build our Relationship-Sensitive Transformer (RSTNet) for image captioning task. The proposed model is tested on the MSCOCO benchmark, where it achieves new state-ofart results on both the Karpathy test split and the online test server.