Video Object Grounding using Semantic Roles in Language Description
Table 3: Total number of lemmatized words (with at least 20 occurrence) in the train set of ASRL.
['V', 'Arg0', 'Arg1', 'Arg2', 'ArgM-LOC']
Verb Figure Arg1 Figure Arg2 The higher number of verbs In comparison, Arg0 is highly unbalanced as agents are mostly restricted to “people”. We also observe that “man” appears much more often than “woman”/“she”. This indicates gender bias in video curation or video description. Another interesting observation is that “person” class dominates in each of argument roles which suggest “person-person” interactions are more commonly described than “person-object” interactions.