Improving Dense Video Captioning with a Transformer-based Multimodal Fusion Model

Dense Video Captioning (DVC) plays a pivotal role in advancing video understanding within computer vision and natural language processing. Traditional DVC models have predominantly focused on visual information, often neglecting the auditory component. To address this limitation, we propose a Transformer-based multimodal fusion model that integrates audio and visual cues for comprehensive multimodal input processing. Built on an encoder-decoder architecture, the model synergizes audio and visual streams. The feature encoder combines self-attention mechanisms with convolutional neural networks to achieve precise audio feature encoding, while the decoder employs multimodal fusion by leveraging intermodal confidence scores to adaptively integrate inputs. A feedforward neural network enhances historical textual representations, and strategic skip connections eliminate redundant data, prioritizing key video features for refined captioning. Extensive validation on benchmark datasets MSR-VTT and MSVD demonstrates that our model outperforms existing methods, achieving BLEU-4, ROUGE, METEOR, and CIDEr scores of 0.427, 0.618, 0.294, and 0.532 on MSR-VTT, and 0.539, 0.741, 0.369, and 0.976 on MSVD. By effectively leveraging the complementary strengths of audio and visual data, our model establishes a new benchmark in DVC, offering precise and comprehensive video content interpretation.