Localizing Moments in Video with Temporal Language
Table 4: Comparison of different model performance for different temporal words on TEMPO - TL on our test set. We report scores for the three temporal words in TEMPO - TL as well as on the original DiDeMo dataset. We find that our model performs best when considering all sentence types. B/A indicated before/after context, WS indicates weak context supervision, and SS indicates strong context supervision.
['[EMPTY]', '[EMPTY]', 'DiDeMo', 'DiDeMo', 'TEMPO - Template Language (TL) Before', 'TEMPO - Template Language (TL) Before', 'TEMPO - Template Language (TL) After', 'TEMPO - Template Language (TL) After', 'TEMPO - Template Language (TL) Then', 'TEMPO - Template Language (TL) Then', '[BOLD] Average', '[BOLD] Average', '[BOLD] Average']
Results: TEMPO - TL. In particular, our model performs well on “before” and “after” words. Additionally, our MLLC model with global context outperforms both the MCN model Hendricks et al.