Permutation Invariant Convolution for Long-Range Video Activity Recognition
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Solution Overview
Problem
Conventional neural networks struggle to accurately recognize long-range activities in videos due to their sensitivity to precise sequential order and inability to learn long-range temporal abstractions.
Innovation Solution
The implementation of a permutation invariant convolution (PIC) layer in a neural network, which segments input streams into frame sets, identifies frames with the highest likelihood of containing specific actions, generates a global representation from pooled frame representations, and classifies long-range activities based on this representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks use convolution, self-attention, or vector aggregation operations, then short-range actions can be classified, but long-range activities cannot be recognized accurately
Solution Approach 1:
The input video stream is segmented into multiple frame sets, where each frame set contains a subset of frames from the original video. This segmentation allows the network to process long-range activities by dividing them into manageable temporal segments, enabling accurate recognition across extended time periods
Solution Approach 2:
The patent introduces a new dimensional approach by creating frame sets that operate in an additional temporal dimension. Instead of processing frames sequentially in one dimension, the network processes multiple frame sets simultaneously, each capturing different temporal perspectives of the same activity
2Reliability
If conventional neural networks are sensitive to precise sequential order, then temporal sequence information is preserved, but long-range temporal abstractions cannot be learned
Solution Approach 1:
The network performs preliminary processing by identifying frames with the highest likelihood of containing specific actions within each frame set before generating global representations. This preliminary action allows the system to capture important temporal events while abstracting away from precise sequential ordering
Solution Approach 2:
The patent changes the parameter of temporal processing by using permutation invariant operations that are insensitive to the exact ordering of frames. This allows the network to learn long-range temporal abstractions while maintaining reliability through frame pooling and global representation generation
3Adaptability or versatility
If frames are pooled to generate global representation, then long-range activity classification is enabled, but computational complexity increases
Solution Approach 1:
The patent extracts only the most relevant information from each frame set by identifying frames with the highest likelihood of containing specific actions. This extraction process reduces the amount of data that needs to be pooled and processed, thereby reducing computational complexity while maintaining global representation capability
Data Source
AI summary
A method for recognizing long-range activities in videos includes segmenting an input video stream to generate multiple frame sets. For each of the frame sets, a frame with a highest likelihood of including one or more actions of a set of predefined actions is identified regardless of its order in the frame set. A global representation of the input stream is generated based on pooled representations of the identified frames. A long-range activity in the video stream is classified based on the global representation.