Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks
Table 1: Evaluation of the proposed object-word alignment weak supervision on the GQA [14] dataset. The presented results are calculated on the dataset’s test-std split. The GQA’s accuracy is presented in the last column. The exact definitions of all other (auxiliary) metrics can be found in [14].
['Models', 'Binary', 'Open', 'Validity', 'Plausibility', 'Consistency', 'Distribution', 'Acc.']
Visual Question Answering – The latter is fundamentally different from our approach (contrary to NSM, our model operates on raw images rather than on the scene graphs). Moreover, it is important to highlight that, unlike previous work, our model has not been finetuned on the target dataset making the obtained result even more significant.