Improving Referring Expression Grounding with Cross-modal Attention-guided Erasing
Table 1: Comparison with state-of-the-art referring expression grounding approaches on ground-truth regions and region proposals from detection model. For RefCOCO and RefCOCO+, testA is for grounding persons, and testB is for grounding objects.
['[EMPTY]', 'test setting', 'RefCOCO val', 'RefCOCO testA', 'RefCOCO testB', 'RefCOCO+ val', 'RefCOCO+ testA', 'RefCOCO+ testB', 'RefCOCOg val∗', 'RefCOCOg val', 'RefCOCOg test']
Quantitative results. CM-Att denotes our model with cross-modal attention trained with only original training samples. CM-Att-Erase denotes our model with cross-modal attention trained with both original samples and erased samples generated by cross-modal attention-guided erasing. It is shown that the cross-modal attention model is already a strong baseline, and training with erased samples can further boost the performance. Our CM-Att-Erase model outperforms previous methods, without increasing inference complexity. It validates that with cross-modal erasing, the model is able to learn better textual-visual correspondences and is better at dealing with comprehensive grounding information.