Multimodal Latent Representation Learning for Video Moment Retrieval.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Artificial intelligence (AI) significantly impacts video sensor data processing for applications like surveillance and autonomous driving.
• Extracting features from video data for AI model training is time-intensive, especially without advanced hardware like GPUs.
• Existing methods face challenges in efficiently processing large video datasets.

Purpose of the Study:

• To introduce a novel framework, the multimodal latent representation learning framework (MLRL), to address limitations in AI video data processing.
• To enhance the performance of downstream AI tasks by performing additional representation learning on pre-extracted features.
• To reduce model training time and improve task accuracy, particularly in resource-constrained research environments.

Main Methods:

• The proposed multimodal latent representation learning framework (MLRL) integrates and augments multimodal data.
• MLRL conducts representation learning on pre-extracted features to predict latent representations.
• The method was validated on the video moment retrieval task using the QVHighlight dataset, benchmarking against the QD-DETR model.

Main Results:

• MLRL demonstrated significant improvements in performance on the video moment retrieval task.
• The framework effectively leverages pre-extracted features to reduce the time-intensive extraction process of raw sensor data.
• Enhanced model accuracy was observed in various sensor-based applications.

Conclusions:

• The multimodal latent representation learning framework (MLRL) offers a viable solution for streamlining AI video data processing.
• MLRL has the potential to make advanced AI research more accessible in environments with limited hardware resources.
• The framework shows promise for improving efficiency and accuracy in diverse AI-driven sensor applications.