Video Adapter for Temporal Feature Extraction in Multimodal Processing
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Solution Overview
Problem
Existing video language pre-training methods face challenges in achieving high accuracy and efficiency for video processing tasks due to the limited availability of high-quality video data and differences between image and video data processing, leading to poor performance when transferring image-language pre-training models to video processing.
Innovation Solution
A video processing method that extracts temporal image features with temporal information from video data, enhancing the characterization capability of image features through a video adapter incorporating temporal networks and dynamic convolutional networks, and performing multi-modal fusion using cross-attention mechanisms to improve alignment and representation of video and language features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image-language pre-training methods are used for video processing, then training data availability is improved, but processing accuracy deteriorates due to differences between image and video data
Solution Approach 1:
The patent segments video data into sequential image frames while preserving temporal relationships. The video processing model divides video input into multiple frames, extracts features from each frame, and then fuses these features with temporal information to maintain both data availability and processing accuracy.
Solution Approach 2:
The patent introduces a video adapter as an intermediary component that bridges image-language pre-training and video processing. This adapter incorporates temporal networks and dynamic convolutional networks to process temporal information from video frames, enabling the transfer of image-language knowledge to video tasks while accounting for temporal dynamics.
2Measurement precision
If temporal information is extracted and integrated into image features, then characterization capability is improved, but model complexity increases
Solution Approach 1:
The patent merges temporal feature extraction with the existing image-language pre-training architecture. The temporal network and dynamic convolutional network are integrated into the video adapter, which combines temporal information from multiple frames with spatial features from individual frames, achieving enhanced characterization without requiring a completely separate complex system.
Solution Approach 2:
The patent performs preliminary extraction of temporal information from video frames before fusion with image features. The temporal network pre-processes temporal relationships by extracting temporal features from sequential frames, and the dynamic convolutional network prepares spatial-temporal features for subsequent fusion, organizing complex temporal data in advance to simplify later processing stages.
3Measurement precision
If video-specific pre-training is conducted to improve accuracy, then processing accuracy is improved, but training costs increase due to limited high-quality video data
Solution Approach 1:
The patent makes the pre-training model universal by enabling it to handle both image and video tasks. The video adapter allows the same base model to process images and videos by adding temporal processing capabilities, eliminating the need for separate pre-training systems and reducing overall training costs while maintaining high accuracy for video-specific tasks.
Solution Approach 2:
The patent changes key parameters in the pre-training process by introducing temporal dimensions. Instead of training on static images alone, the model processes sequences of frames with temporal relationships, adjusting learning parameters and feature extraction mechanisms to accommodate video-specific characteristics while leveraging existing image-language pre-training foundations.
Data Source
AI summary
The present disclosure provides a video processing method, apparatus, device, storage medium, and program product. The method includes: acquiring video data; obtaining, based on the video data, a temporal image feature with temporal information; determining, based on the temporal image feature, a target text feature in a set of text features that matches the temporal image feature; and obtaining, based on the target text feature, target text data corresponding to the video data.