Multimodal Video-Text Feature Fusion for Fine-Grained Alignment
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Solution Overview
Problem
The challenge in multi-modal video understanding is the significant variation in content feature representation between different modalities, particularly between videos and text, necessitating a model that can effectively process and integrate diverse input data.
Innovation Solution
A data processing method that involves obtaining feature representations for videos and texts using encoders, fusing these representations, and performing contrastive learning to align and update the encoders, ensuring consistent processing granularity and incorporating both local and global information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature extraction is performed with text as a whole only, then the text processing is simple, but the processing granularity is coarse and precision is reduced
Solution Approach 1:
The text is segmented into multiple text units (e.g., sentences or paragraphs) instead of processing as a whole. This segmentation enables finer-grained feature extraction at the text unit level, improving processing precision while maintaining manageable complexity through modular processing of individual units.
Solution Approach 2:
Different processing granularities are applied to different parts of the text. Text units are processed at a finer granularity level, while the overall text structure is maintained at a coarser level. This local quality approach allows precise local feature extraction without sacrificing global context understanding.
2Measurement precision
If only local context information is used for text units, then the local feature extraction is accurate, but global text information is lost
Solution Approach 1:
The feature representations from multiple text units are merged with the overall text feature representation. This combining process integrates both local information from individual text units and global information from the complete text, ensuring that neither local precision nor global context is lost in the final feature representation.
Solution Approach 2:
The feature extraction process uses a nested structure where text unit features are nested within the overall text feature representation. Each text unit's local features are embedded in the broader global context, creating a hierarchical structure that preserves both local and global information at appropriate levels.
3Ease of operation
If different processing granularities are used for image and text branches, then processing is simplified, but feature alignment between modalities deteriorates
Solution Approach 1:
The patent applies different processing granularities at different levels: the image branch processes at the frame level while the text branch processes at the text unit level. This local quality approach allows each modality to be processed at its optimal granularity while maintaining alignment through the contrastive learning mechanism that operates across these different levels.
Solution Approach 2:
The processing granularity parameter is changed differently for each modality based on its characteristics. Images are processed at frame level granularity while text is processed at text unit granularity. This parameter adaptation allows each modality to be processed optimally while the contrastive learning framework ensures their features remain aligned despite the granularity difference.
Data Source
AI summary
A data processing method is disclosed, is applied to the field of video understanding in artificial intelligence, and includes: obtaining a video and text, where the text includes a plurality of text units; obtaining a first feature representation of the video based on the video by using an image encoder; obtaining, based on the text by using a text encoder, a second feature representation of each text unit and a third feature representation corresponding to the text, where the third feature representation is obtained by performing feature extraction by using the text encoder with the text as a whole; fusing the third feature representation and each second feature representation, to obtain a plurality of fourth feature representations; and performing contrastive learning between the first feature representation and the plurality of fourth feature representations, to update the image encoder and the text encoder.