MAttNet: Modular Attention Network for Referring Expression Comprehension
Table 4: Comparison of segmentation performance on RefCOCO, RefCOCO+, and our results on RefCOCOg.
['RefCOCOg Model', 'RefCOCOg Backbone Net', 'RefCOCOg Split', 'RefCOCOg Pr@0.5', 'RefCOCOg Pr@0.6', 'RefCOCOg Pr@0.7', 'RefCOCOg Pr@0.8', 'RefCOCOg Pr@0.9', 'RefCOCOg IoU']
(res101-mrcn). We apply the same procedure described in Sec. Then we feed the predicted bounding box to the mask branch to obtain a pixel-wise segmentation. We use Precision@X (X∈{0.5,0.6,0.7,0.8,0.9}) Results are shown in Table. Some referential segmentation examples are shown in Fig. Note this is not a strictly fair comparison as our model was trained with fewer images. Overall, the AP of our implementation is ∼2 points lower. The main reason may due to the shorter 600-pixel edge setting and smaller training batch size. Even though, our pixel-wise comprehension results already outperform the state-of-the-art ones with a huge margin (see Table. and we believe there exists space for further improvements.