  Existing video captioning benchmarks and models lack coherent representations
of causal-temporal narrative, which is sequences of events linked through cause
and effect, unfolding over time and driven by characters or agents. This lack
of narrative restricts models' ability to generate text descriptions that
capture the causal and temporal dynamics inherent in video content. To address
this gap, we propose NarrativeBridge, an approach comprising of: (1) a novel
Causal-Temporal Narrative (CTN) captions benchmark generated using a large
language model and few-shot prompting, explicitly encoding cause-effect
temporal relationships in video descriptions, evaluated automatically to ensure
caption quality and relevance; and (2) a dedicated Cause-Effect Network (CEN)
architecture with separate encoders for capturing cause and effect dynamics
independently, enabling effective learning and generation of captions with
causal-temporal narrative. Extensive experiments demonstrate that CEN is more
accurate in articulating the causal and temporal aspects of video content than
the second best model (GIT): 17.88 and 17.44 CIDEr on the MSVD and MSR-VTT
datasets, respectively. The proposed framework understands and generates
nuanced text descriptions with intricate causal-temporal narrative structures
present in videos, addressing a critical limitation in video captioning. For
project details, visit https://narrativebridge.github.io/.
