Towards Debiasing Frame Length Bias in

Text-Video Retrieval via Causal Intervention

Burak Satar $^{1,2}$, Hongyuan Zhu$^{1}$, Hanwang Zhang$^{2}$, Joo Hwee Lim$^{1,2}$
$^{1}$Institute of Infocomm Research, A*STAR | $^{2}$SCSE, Nanyang Technological University
British Machine Vision Conference (BMVC), 2023

Structural Causal Model


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.




author    = {Burak Satar and Hongyuan Zhu and Hanwang Zhang and Joo-Hwee Lim},
title     = {Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal Intervention},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year      = {2023},
url       = {}


This research is supported by the Agency for Science, Technology and Research (A*STAR)
under its AME Programmatic Funding Scheme (Project A18A2b0046).