Video Classification Neural Network Parameter Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video classification systems using neural networks face challenges in effectively combining image information across videos while maintaining a manageable number of parameters, which affects their ability to accurately classify videos based on temporal evolution.
Innovation Solution
The implementation of Long Short-Term Memory (LSTM) layers or pooling layers in neural network architectures to process sequences of video frames, generating classifications by combining convolutional features and maintaining a constant, non-excessive number of parameters, allowing for effective video classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks use more parameters to effectively combine image information across videos, then classification accuracy improves, but the number of parameters becomes excessively large
Solution Approach 1:
The patent segments the video processing task into multiple stages: extracting individual frame features, generating temporal representations through pooling operations, and combining these representations for classification. This segmentation allows the system to process video data in manageable portions rather than requiring all parameters to process the entire video at once, thereby maintaining accuracy while controlling parameter count.
Solution Approach 2:
The patent introduces temporal pooling operations that operate in the time dimension, combining features across multiple frames. This dimensional approach allows the system to capture temporal evolution without proportionally increasing spatial parameter counts, as the pooling operations aggregate information across time rather than requiring separate parameters for each temporal relationship.
2Measurement precision
If neural networks process more video frames to capture temporal evolution, then classification accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction on individual video frames before temporal aggregation. By pre-processing each frame to extract relevant features and then combining these features through pooling operations, the system avoids the computational burden of processing complete video sequences through the entire network, thereby reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The patent uses pooling operations that aggregate features from multiple frames, applying a form of partial action where not all frame details are processed in full detail. The pooling operations selectively combine features, capturing essential temporal information while discarding redundant details, thus achieving good classification performance with reduced processing time compared to exhaustive frame-by-frame analysis.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying videos using neural networks. One of the methods includes obtaining a temporal sequence of video frames, wherein the temporal sequence comprises a respective video frame from a particular video at each of a plurality time steps; for each time step of the plurality of time steps: processing the video frame at the time step using a convolutional neural network to generate features of the video frame; and processing the features of the video frame using an LSTM neural network to generate a set of label scores for the time step and classifying the video as relating to one or more of the topics represented by labels in the set of labels from the label scores for each of the plurality of time steps.