Cross-modal Processing Model for Temporal Alignment
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Solution Overview
Problem
Existing visual-text models struggle with temporal semantic representations and correlations between images and videos, failing to learn temporal understanding capabilities in the pre-training stage due to limited data and high visual redundancy in video-text corpora, leading to low accuracy and efficiency in cross-modal data processing.
Innovation Solution
A cross-modal data processing method that pre-trains a processing model using concatenated image and text samples, maintaining temporal sequence correspondence and providing rich scene transition information, enabling explicit scene-level time alignment and improved learning of static and temporal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video-text corpora are used for pre-training, then cross-modal data processing capability is improved, but visual redundancy increases and learning efficiency decreases
Solution Approach 1:
The patent segments video data into discrete frame sequences, where each frame is processed independently through the cross-modal processing model. This segmentation approach reduces visual redundancy by focusing on individual frame-text correlations rather than processing entire video sequences as monolithic units, thereby improving learning efficiency while maintaining cross-modal processing capability.
2Measurement precision
If existing visual-text models are used, then processing speed is maintained, but temporal semantic representation accuracy decreases
Solution Approach 1:
The patent performs preliminary feature extraction on video frames before feeding them into the cross-modal processing model. By pre-processing and organizing frame features in advance, the system achieves accurate temporal semantic representation through structured frame sequences while maintaining processing speed through efficient feature representation.
3Measurement precision
If concatenated training samples with scene transition information are used, then scene-level time alignment accuracy is improved, but training data complexity increases
Solution Approach 1:
The patent introduces scene transition information as an intermediary element that bridges video frames and text descriptions. This intermediary provides explicit temporal cues that improve scene-level time alignment accuracy while managing training data complexity through structured annotation formats that capture temporal relationships efficiently.
Data Source
AI summary
The disclosure provides a cross-modal data processing method and apparatus, a device, a storage medium, and a program product. The method comprises: obtaining first modal data to be processed; obtaining a first modal data feature by performing feature extraction based on the first modal data; and obtaining second modal data based on the first modal data feature and a cross-modal processing model, the first modal data and the second modal data having different modalities, wherein the cross-modal processing model needs to be pre-trained based on a concatenated training sample, and the concatenated training sample comprises a concatenated image sample and a corresponding concatenated text sample.