  Dynamic scene graph generation (SGG) from videos requires not only a
comprehensive understanding of objects across scenes but also a method to
capture the temporal motions and interactions with different objects. Moreover,
the long-tailed distribution of visual relationships is a crucial bottleneck
for most dynamic SGG methods. This is because many of them focus on capturing
spatio-temporal context using complex architectures, leading to the generation
of biased scene graphs. To address these challenges, we propose FloCoDe:
Flow-aware Temporal Consistency and Correlation Debiasing with uncertainty
attenuation for unbiased dynamic scene graphs. FloCoDe employs feature warping
using flow to detect temporally consistent objects across frames. To address
the long-tail issue of visual relationships, we propose correlation debiasing
and a label correlation-based loss to learn unbiased relation representations
for long-tailed classes. Specifically, we propose to incorporate label
correlations using contrastive loss to capture commonly co-occurring relations,
which aids in learning robust representations for long-tailed classes. Further,
we adopt the uncertainty attenuation-based classifier framework to handle noisy
annotations in the SGG data. Extensive experimental evaluation shows a
performance gain as high as 4.1%, demonstrating the superiority of generating
more unbiased scene graphs.
