Spatiotemporal Video-Text Matching for Sequence Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video processing methods fail to effectively match video sequences with text due to a lack of consideration for temporal associations, resulting in a low degree of matching and inadequate understanding of video content.
Innovation Solution
A method that extracts spatiotemporal candidate regions from videos, performs feature extraction using convolutional neural networks, and utilizes an attention-based interactor to determine matching scores, thereby considering the temporal association between videos and text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single frame matching with text is performed, then the processing complexity is reduced, but the matching degree between video sequence and text deteriorates
Solution Approach 1:
The patent transitions from single-frame spatial matching to spatiotemporal matching by introducing the time dimension. Video sequences are segmented into multiple frames that are processed together to capture temporal associations, transforming the matching problem from 2D (spatial) to 3D (spatiotemporal) space, thereby improving matching accuracy without excessive complexity increase
Solution Approach 2:
The video is segmented into multiple frames that are processed individually and then aggregated. Each frame is matched with text independently, and the results are combined to form the overall video-text matching score, allowing the system to handle complex spatiotemporal relationships through manageable frame-level operations
2Measurement precision
If spatiotemporal candidate regions are extracted and matched, then the matching degree between video sequence and text is improved, but the processing complexity increases
Solution Approach 1:
The video is divided into multiple frames that are processed independently. Each frame is matched with the text separately, and the individual frame matching results are aggregated to produce the final video-text matching score. This segmentation approach reduces the overall computational complexity by breaking down the complex spatiotemporal matching problem into simpler frame-level tasks
Solution Approach 2:
The patent performs matching on selected key frames rather than every single frame in the video. By identifying and processing only the most relevant frames for text matching, the system achieves effective spatiotemporal association while avoiding the excessive computational burden of processing all frames, thus balancing accuracy and efficiency
Data Source
Figure 1~2
Figure 3
Figure 4~5
AI summary
Disclosed is a video sequence selection method, applied to a computer device. The method comprises: receiving a video to be matched and text to be matched, wherein the text to be matched corresponds to a text feature sequence to be matched; calling a spatio-temporal candidate region generator to extract a spatio-temporal candidate region set from the video to be matched, wherein the spatio-temporal candidate region set comprises N spatio-temporal candidate regions; performing feature extraction on each spatio-temporal candidate region by means of a convolutional neural network to obtain N video feature sequences to be matched; calling an attention-based interactive device to acquire a matching score corresponding to each spatio-temporal candidate region, wherein the matching score is used for indicating a matching relationship between the spatio-temporal candidate region and the text to be matched; and selecting, according to the matching score corresponding to each spatio-temporal candidate region, a target spatio-temporal candidate region from the spatio-temporal candidate region set, and outputting the target spatio-temporal candidate region. In the present application, a time sequence correlation between a video and text is taken into consideration during matching, thereby improving the matching degree of a video sequence and the text.