Multi-Scale Receptive Field Aggregation for Video Classification
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Solution Overview
Problem
Existing video classification methods fail to accurately classify video clips due to irrelevant or trivial information in each frame, affecting classification accuracy.
Innovation Solution
A method and system that aggregates information from multi-scale first and second dimensional receptive fields using convolutional neural networks with local constraints to focus on important regions, enhancing the context and improving classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing video classification methods process each frame independently, then processing speed is maintained, but classification accuracy deteriorates due to irrelevant or trivial information
Solution Approach 1:
The video is segmented into multiple scales of receptive fields (first dimensional and second dimensional) to process different spatial and temporal information at different granularities. This segmentation allows the system to focus on relevant information at appropriate scales while filtering out irrelevant details, thereby improving classification accuracy without sacrificing processing efficiency
Solution Approach 2:
Different regions of the video frames are assigned different processing qualities through the multi-scale receptive field mechanism. Important regions (those within the object trajectories) receive focused attention through constrained convolutional operations, while irrelevant regions are downweighted or ignored. This local quality differentiation eliminates interference from trivial information while maintaining accurate classification
2Loss of information
If multi-scale receptive field aggregation is implemented, then context information is improved, but computational complexity increases
Solution Approach 1:
The computational task is segmented into two independent convolutional neural network operation sets (first and second dimensional), each handling specific spatial or temporal constraints. This segmentation allows parallel processing of different receptive field dimensions, reducing overall computational complexity while maintaining complete context information through the aggregation of both dimensional results
Solution Approach 2:
Instead of processing all possible spatial and temporal combinations exhaustively, the method applies partial action by using constrained convolutional operations that only process relevant portions of the video data at each scale. The local-to-local constraints limit computations to necessary regions, achieving sufficient context information without excessive computational burden
3Measurement precision
If convolutional neural network operations with local constraints are used, then important regions are focused on, but processing speed decreases
Solution Approach 1:
The processing is organized into periodic stages corresponding to different receptive field scales. At each periodic stage, the convolutional operations with local constraints are applied systematically across the video data. This periodic structure allows the system to maintain focus on important regions while managing processing speed through organized, repetitive computation patterns that can be optimized and parallelized
Data Source
AI summary
A method includes: aggregating information from a corresponding combination of all of multi-scale first dimensional receptive fields and each of multi-scale second dimensional receptive fields, so that information from multi-scale first and second dimensional receptive fields corresponding to the multi-scale second dimensional receptive fields is obtained; wherein the multi-scale first dimensional receptive fields being first dimensional is being one of spatial and temporal, and the multi-scale second dimensional receptive fields being second dimensional is being the other of spatial and temporal; wherein a corresponding first convolutional neural network operation set provides each of the multi-scale second dimensional receptive fields and each operation of the first convolutional neural network operation set has a corresponding first dimensional local to local constraint; and aggregating the information from the multi-scale first and second dimensional receptive fields to obtain aggregated multi-scale first and second dimensional receptive field information.