Bridging Vision and Language: Modeling Causality and Temporality in Video Narratives
This work addresses the challenge of generating coherent and causally-aware captions for videos, which is important for applications in multimodal AI, though it appears incremental as it builds upon existing LVLMs.
The paper tackled the problem of video captioning by addressing the limitations of large vision-language models in capturing causal and temporal dynamics, resulting in a method that outperforms existing approaches on benchmarks like MSVD and MSR-VTT with improved metrics such as CIDEr, BLEU-4, and ROUGE-L.
Video captioning is a critical task in the field of multimodal machine learning, aiming to generate descriptive and coherent textual narratives for video content. While large vision-language models (LVLMs) have shown significant progress, they often struggle to capture the causal and temporal dynamics inherent in complex video sequences. To address this limitation, we propose an enhanced framework that integrates a Causal-Temporal Reasoning Module (CTRM) into state-of-the-art LVLMs. CTRM comprises two key components: the Causal Dynamics Encoder (CDE) and the Temporal Relational Learner (TRL), which collectively encode causal dependencies and temporal consistency from video frames. We further design a multi-stage learning strategy to optimize the model, combining pre-training on large-scale video-text datasets, fine-tuning on causally annotated data, and contrastive alignment for better embedding coherence. Experimental results on standard benchmarks such as MSVD and MSR-VTT demonstrate that our method outperforms existing approaches in both automatic metrics (CIDEr, BLEU-4, ROUGE-L) and human evaluations, achieving more fluent, coherent, and relevant captions. These results validate the effectiveness of our approach in generating captions with enriched causal-temporal narratives.