We hypothesize that due to pretrained word embeddings (which include embeddings for words describing the unknown categories), the coreference models will be able to successfully ground to new object categories.
the performance of the Vision model will decrease in the HARD split, because the pretrained visual features of the new objects are not well aligned with representations of unseen words.