Video Clip Localization Using Semantic-Enhanced Frame Features
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Solution Overview
Problem
The challenge of accurately locating a video clip based on a natural language description is more complex compared to detecting a static image, necessitating improved methods for video processing.
Innovation Solution
A video processing method that involves obtaining a semantic feature of an input sentence, performing semantic enhancement on video frames to fuse semantic meaning into their features, and determining the target video clip through semantic and video features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic enhancement is performed on video frames to fuse semantic meaning into video features, then the accuracy of recognizing the target video clip is improved, but the computational complexity and processing time increase
Solution Approach 1:
The video processing is divided into discrete temporal segments (video frames) that are processed independently through the semantic enhancement module. Each frame undergoes separate semantic feature fusion with the input sentence, allowing parallel processing and reducing overall computational complexity while maintaining recognition accuracy.
Solution Approach 2:
The semantic features of the input sentence are extracted and prepared in advance before video frame processing begins. This preliminary action allows the semantic enhancement module to directly fuse pre-computed semantic features with video frame features during processing, reducing real-time computational burden while improving recognition accuracy.
2Measurement precision
If semantic enhancement is performed on video frames to fuse semantic meaning into video features, then the accuracy of recognizing the target video clip is improved, but the processing time increases
Solution Approach 1:
The video is segmented into individual frames that can be processed in parallel through the semantic enhancement module. This frame-level segmentation enables concurrent processing of multiple frames, reducing total processing time while maintaining the accuracy improvements from semantic feature fusion.
Solution Approach 2:
Semantic feature extraction from the input sentence is performed as a preliminary action before video processing. This pre-computation of semantic features eliminates the need for repeated semantic analysis during frame processing, significantly reducing processing time while preserving the accuracy benefits of semantic enhancement.
Data Source
AI summary
A video clip location technology in the field of computer vision pertaining to artificial intelligence that provides a video processing method and apparatus. The method includes: obtaining a semantic feature of an input sentence; performing semantic enhancement on a video frame based on the semantic feature to obtain a video feature of the video frame, where the video feature includes the semantic feature; and determining, based on the semantic feature and the video feature, whether a video clip to which the video frame belongs is a target video clip corresponding to the input sentence. The method helps improve accuracy of recognizing a target video clip corresponding to an input sentence.