Hierarchy Parsing for Image Captioning
Table 4: Performance comparisons on recognition task when employing different features.
['[EMPTY]', 'C-P', 'C-R', 'C-F1', 'O-P', 'O-R', 'O-F1']
Extension to Recognition. As a feature refiner, here we test the generalizability of our HIP on recognition task. We also experiment with COCO dataset on 80 object categories and utilize object annotations as multiple labels of an image. For each image, we predict top-3 ranked labels. Then, we compute the precision and recall for each label separately, and report per-class precision (C-P) and pre-class recall (C-R). Furthermore, to alleviate the bias towards infrequent labels, we also compute overall precision (O-P) and overall recall (O-R). As the harmonic average of precision and recall, F1 (C-F1 and O-F1) scores are given as well. By delving into hierarchy parsing, image-level features from HIP lead to 2% and 3.2% performance gain in C-F1 and O-F1 over the features in Up-Down. The results basically verify the generalizability of HIP on recognition task.