Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal
  Intervention
Burak Satar Hongyuan Zhu Hanwang Zhang Joo Hwee Lim
Many studies focus on improving pretraining or developing new backbones in
text-video retrieval. However, existing methods may suffer from the learning
and inference bias issue, as recent research suggests in other
text-video-related tasks. For instance, spatial appearance features on action
recognition or temporal object co-occurrences on video scene graph generation
could induce spurious correlations. In this work, we present a unique and
systematic study of a temporal bias due to frame length discrepancy between
training and test sets of trimmed video clips, which is the first such attempt
for a text-video retrieval task, to the best of our knowledge. We first
hypothesise and verify the bias on how it would affect the model illustrated
with a baseline study. Then, we propose a causal debiasing approach and perform
extensive experiments and ablation studies on the Epic-Kitchens-100, YouCook2,
and MSR-VTT datasets. Our model overpasses the baseline and SOTA on nDCG, a
semantic-relevancy-focused evaluation metric which proves the bias is
mitigated, as well as on the other conventional metrics.