LGD Model Spatio-Temporal Video Recognition
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Solution Overview
Problem
Current methods for video recognition, such as directly applying 2D CNNs to video frames or 3D CNNs to video segments, face challenges in effectively capturing spatio-temporal representations, leading to suboptimal recognition accuracy.
Innovation Solution
A method and apparatus utilizing a pre-trained local and global diffusion (LGD) model that learns spatio-temporal representations by diffusing between local and global representations, incorporating cascaded LGD modules, a local and global combination classifier, and a fully connected layer to improve video recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If 2D CNN is directly applied to video frames, then the implementation is simple, but the spatio-temporal representation capability is insufficient
Solution Approach 1:
The patent combines 2D CNN spatial feature extraction with temporal modeling components (such as RNN, LSTM, or temporal convolutional layers) to create a hybrid architecture that captures both spatial and temporal dependencies in video data, resolving the contradiction between implementation simplicity and spatio-temporal representation capability
Solution Approach 2:
The patent extends the 2D CNN architecture by adding a temporal dimension, transforming it into a 3D CNN or by stacking multiple 2D CNNs with temporal pooling/convolution operations, thereby enhancing spatio-temporal representation while building upon the simple 2D CNN foundation
2Measurement precision
If 3D CNN is applied to video segments, then the spatio-temporal representation is improved, but the computational complexity increases
Solution Approach 1:
The patent divides video data into smaller segments or clips and processes them independently through the 3D CNN architecture, reducing the computational burden on any single processing unit while maintaining comprehensive spatio-temporal analysis across the entire video sequence
Solution Approach 2:
The patent applies 3D CNN selectively to key video segments or regions of interest rather than processing the entire video uniformly, reducing overall computational complexity while maintaining high spatio-temporal representation capability where most needed
Data Source
AI summary
Disclosed are a method and device for recognizing a video. One specific embodiment of the method comprises: obtaining a video to be identified; inputting said video to a pre-trained local and global representation propagation LGD model to obtain the category of said video, wherein the LGD model learns a spatial-temporal representation in said video based on diffusion between local and global representations. According to this embodiment, the spatial-temporal representation in the video is learned based on diffusion between the local and global representations.