Dense Video Captioning

[GitHub]

• Tech Stack: Python, GitHub, AWS, GCP
• Summary

  • Utilizing PDVC utilizing a LLM model

  • This project presents an innovative approach to enhance dense video captioning using language models for semantic alignment. Integrating a tuner network into the Parallel Decoding for Dense Video Captioning (PDVC) model significantly improves performance across multiple evaluation metrics. The study underscores the importance of semantic alignment in generating accurate and engaging video captions.

  • This project leverages language models to enhance dense video captioning through semantic alignment introduced via a tuner network. The modified PDVC model consistently outperforms the baseline across diverse evaluation metrics, highlighting the critical role of proper alignment in caption quality. The outcomes set the stage for future advancements in this domain.

• In-Depth

  • Abstract
    In recent years, dense video captioning has gained significant attention across domains like autonomous driving, video surveillance, and accessibility. This project introduces a novel approach to improve dense video captioning performance by leveraging language models (LM) for semantic alignment of video features. The project's focus is on comparing this approach with the baseline model, Parallel Decoding for Dense Video Captioning (PDVC), which has demonstrated strong results in the realm of video captioning. The study utilizes the YouCook2 dataset due to computational limitations. Semantic alignment is achieved by introducing a tuner network before the PDVC framework, employing different tuner architectures. The modified PDVC with semantic alignment consistently outperforms the baseline PDVC in various evaluation metrics. The outcomes of this research lay the foundation for future extensions and applications.

  • Methodology
    The project's methodology begins with establishing the baseline performance of PDVC on the YouCook2 dataset. Subsequently, the study introduces a tuner network prior to the PDVC framework, focusing on different tuner architectures. The tuner is trained and then integrated into the PDVC structure for evaluation. Evaluation metrics encompass BLEU4, METEOR, CIDEr, and Soda_c, offering a comprehensive assessment of captioning quality and alignment effectiveness. The performance of various tuner architectures is compared against the baseline and each other to identify improvements and shortcomings.

  • Results
    The results are summarized in Table 2, presenting outcomes for both the baseline PDVC and the modified PDVC using different tuner architectures. While minor variations exist in baseline results, they closely align with reported figures in previous work. The tuner network's impact on PDVC is profound. The Linear, Conv1, and Conv1 w/ Linear tuner architectures consistently outperform the baseline PDVC in multiple metrics, with Conv1 exhibiting superiority across all metrics. Conversely, Conv2 exhibits notably lower scores across all metrics, possibly due to constrained model parameters acting as a bottleneck in semantic alignment.

  • Discussion
    The study underscores the substantial impact of semantic alignment on dense video captioning performance. Models incorporating semantic alignment consistently achieve higher scores across evaluation metrics, indicative of enhanced caption generation. Nonetheless, the study acknowledges metric score variance, attributed to the relatively limited YouCook2 dataset. This highlights the significance of optimal training data size and quality to ensure consistent performance. Additionally, the study emphasizes the detrimental effect of improperly trained tuner models, exemplified by Conv2, stressing the need for fine-tuning tuner architectures. The study promotes the integration of semantic alignment in video captioning and provides insights into architectural strengths and weaknesses for potential model refinement.

  • Future Work
    The project outlines promising directions for future research in dense video captioning. Expanding the model to larger and more diverse datasets, such as ActivityNet, could enhance generalizability and stability across evaluation metrics. Investigating alternative tuner architectures, including transformer-based models and attention mechanisms, presents avenues for performance enhancement. The study suggests exploring different text encoders for improved semantic alignment and experimenting with dimension-matching methods to preserve information. Ultimately, the project's findings contribute to the advancement of dense video captioning models and emphasize the significance of semantic alignment in such models. The research sets the stage for further exploration and innovation in this domain.