Graph-Structured Referring Expression Reasoning in The Wild
Table 1: Comparison with baselines and state-of-the-art methods on Ref-Reasoning dataset. We use * to indicate that this model uses bottom-up features and use ‡ to indicate that the implementation of this model is slightly different from it in origin paper. The best performing method is marked in bold.
['[EMPTY]', 'Number of Objects one', 'Number of Objects two', 'Number of Objects three', 'Number of Objects >= four', 'Split val', 'Split test']
The proposed SGMN significantly outperforms the baselines and existing state-of-the-art models, and it consistently achieves the best performance on all the splits of the testing set, where different splits need different numbers of reasoning steps. The CNN+LSTM model achieves a high accuracy of 75.29% on the split where expressions directly describe the referents. This is because relation reasoning is not required in this split and LSTM may be qualified to capture the semantics of expressions. Compared with the CNN+LSTM model, DGA and CMRIN achieve higher performance on the two-, three- and four-node splits because they learn a language-guided contextual representation for objects.