VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval (2412.01558v1)

Abstract: Video Highlight Detection and Moment Retrieval (HD/MR) are essential in video analysis. Recent joint prediction transformer models often overlook their cross-task dynamics and video-text alignment and refinement. Moreover, most models typically use limited, uni-directional attention mechanisms, resulting in weakly integrated representations and suboptimal performance in capturing the interdependence between video and text modalities. Although large-language and vision-LLMs (LLM/LVLMs) have gained prominence across various domains, their application in this field remains relatively underexplored. Here we propose VideoLights, a novel HD/MR framework addressing these limitations through (i) Convolutional Projection and Feature Refinement modules with an alignment loss for better video-text feature alignment, (ii) Bi-Directional Cross-Modal Fusion network for strongly coupled query-aware clip representations, and (iii) Uni-directional joint-task feedback mechanism enhancing both tasks through correlation. In addition, (iv) we introduce hard positive/negative losses for adaptive error penalization and improved learning, and (v) leverage LVLMs like BLIP-2 for enhanced multimodal feature integration and intelligent pretraining using synthetic data generated from LVLMs. Comprehensive experiments on QVHighlights, TVSum, and Charades-STA benchmarks demonstrate state-of-the-art performance. Codes and models are available at https://github.com/dpaul06/VideoLights .

• The paper introduces VideoLights, a transformer model that integrates feature refinement, bi-directional fusion, and joint-task feedback for improved video highlight detection and moment retrieval.
• It employs a Feature Refinement and Alignment module, a bi-directional cross-modal fusion network, and adaptive loss functions to optimize the alignment between video and text features.
• Empirical evaluations on QVHighlights, TVSum, and Charades-STA demonstrate state-of-the-art performance across key metrics, offering scalable benefits for real-time video analysis.

Analysis of "VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval"

The paper VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval proposes an innovative framework aimed at enhancing the accuracy and efficiency of video analysis tasks. The central contributions of the research lie in the development of a sophisticated transformer model, VideoLights, which effectively addresses the complexities of integrating Video Highlight Detection (HD) and Moment Retrieval (MR) within a unified architecture.

Core Contributions and Methodology

At its core, VideoLights introduces a multi-component architecture integrating several novel techniques to refine and synergize video and text modalities:

1. Feature Refinement and Alignment (FRA) Module: This module employs convolutional layers for feature projection, enhancing the alignment between video clips and the text descriptions. It ensures both local and global alignment by refining video features in accordance with textual information, significantly improving the accuracy of video-text correspondence.
2. Bi-Directional Cross-Modal Fusion (Bi-CMF) Network: Unlike conventional uni-directional models, Bi-CMF incorporates a dual-stage attention mechanism. This facilitates robust bi-directional interactions between video and text features, promoting a more coherent and query-aware representation of video content.
3. Unidirectional Joint-Task Feedback Mechanism (Uni-JFM): This mechanism leverages feedback from task-specific computations, optimizing the cross-task learning processes between HD and MR. It introduces a task-coupled loss strategy which reinforces the correlation between detected highlights and retrieved moments.
4. Adaptive Loss Functions: The framework introduces hard positive and hard negative losses, tailored to dynamically adjust the learning process. These losses focus on misalignment corrections, refining the saliency predictions to mitigate persistent errors in highlight ranking.
5. Synthetic Data Pretraining: The integration of Vision-LLMs (LVLMs) like BLIP-2 enables the generation of synthetic training data, significantly enriching the pretraining phase. This approach generates high-quality semantic data, effectively compensating for the limitations of traditional caption-based pretraining methodologies.

Empirical Evaluation

The efficacy of VideoLights is thoroughly validated through experiments conducted on the QVHighlights, TVSum, and Charades-STA benchmark datasets. The results illustrate superior performance across both HD and MR tasks, with VideoLights achieving the new state-of-the-art in these domains. The testing revealed improvements across all key metrics, showcasing the framework's ability to handle various intricate aspects of video analysis more adeptly than existing methods.

Implications and Future Directions

This research has notable implications for the field of video processing and multimodal data integration. Primarily, the dual application design of VideoLights presents a scalable solution for real-time video content management, where accurate detection and retrieval can streamline applications in digital content curation, surveillance, and automated editing tools.

Furthermore, the successful integration of LVLMs into the training regimens signals a promising direction for future endeavors, suggesting that similar approaches could significantly enhance model performance in other domains involving multimodal data fusion. Future developments may explore extending these techniques to facilitate contextual understanding and reasoning in AI systems, potentially yielding more intelligent and context-aware video analysis solutions.

Conclusion

The paper delivers substantial advancements in the challenging field of joint video analysis tasks, proposing methodologies that not only improve performance metrics but also offer innovative paradigms for understanding cross-modal interactions. VideoLights stands as a robust framework poised to enhance the integration of multimodal knowledge, underscoring significant potential for future innovations in video analytics and AI-driven multimedia technologies.