DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Video content proliferation necessitates efficient video moment retrieval and highlight detection.
• Existing methods struggle with ambiguous temporal event boundaries, leading to performance degradation.
• Discriminative learning approaches often involve complex cross-modal interactions for query-activity correspondence.

Purpose of the Study:

• To propose a novel framework, DiffusionVMR, for jointly addressing video moment retrieval and highlight detection.
• To overcome the challenge of unclear temporal boundaries in video content.
• To enhance the accuracy and robustness of temporal event localization.

Main Methods:

• A unified conditional denoising generation process is proposed, leveraging diffusion models.
• The framework, DiffusionVMR, redefines moment retrieval and highlight detection as a unified task.
• Training involves corrupting ground truth with Gaussian noise and training the model to reverse the process; inference starts from noise and iteratively refines results.

Main Results:

• DiffusionVMR demonstrates effectiveness and flexibility across five benchmarks for both moment retrieval and highlight detection.
• The iterative refinement capability of diffusion models enhances coarse-to-fine boundary transitions.
• Decoupled training and inference allow for arbitrary inference settings.

Conclusions:

• The proposed denoising generation perspective effectively alleviates boundary ambiguity in video analysis.
• DiffusionVMR offers a robust and flexible solution for video moment retrieval and highlight detection.
• The framework's ability to iteratively refine results improves localization accuracy.