RL Sequence Processing for Skipping Redundant Video Frames
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Solution Overview
Problem
Existing neural networks face inefficiencies in processing sequences of task inputs, particularly in tasks like action recognition from video frames, due to the lack of effectively addressing temporal redundancy and requiring costly processing resources.
Innovation Solution
Utilizing a reinforcement learning neural network system to determine which frames to encode or skip the task inputs, particularly in tasks like action recognition from video frames, particularly in tasks like action recognition, due to the lack of effectively addressing temporal redundancy and requiring costly processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is applied to improve sequence processing adaptability, then adaptability improves, but computational complexity and training time increase
Solution Approach 1:
The sequence processing task is segmented into fixed-length chunks that are processed independently by the neural network. This segmentation allows the model to handle variable-length sequences through multiple sequential passes, reducing the computational complexity of each individual processing step while maintaining overall adaptability.
Solution Approach 2:
The sequence is pre-processed by dividing it into fixed-length segments before being fed to the neural network. This preliminary action simplifies the neural network's processing burden by providing consistently sized inputs, enabling more efficient computation while preserving the ability to handle variable-length original sequences through multiple passes.
2Speed
If fixed sequence length is used for neural network input, then processing speed improves, but information loss increases
Solution Approach 1:
The system maintains continuous processing by repeatedly applying the fixed-length window transformation across the entire sequence. Each position in the original sequence serves as a starting point for a new fixed-length segment, ensuring that all information is processed through multiple overlapping passes, thereby preventing information loss while maintaining consistent processing speed.
Solution Approach 2:
The approach transforms the problem from processing the entire variable-length sequence in one pass to processing multiple fixed-length segments across different positions. This dimensional transformation allows the system to maintain fixed processing window sizes while comprehensively covering all sequence information through systematic repositioning and repeated processing.
Data Source
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AI summary
A system that is configured to receive a sequence of task inputs and to perform a machine learning task is described. The system includes a reinforcement learning (RL) neural network and a task neural network. The RL neural network is configured to: generate, for each task input of the sequence of task inputs, a respective decision that determines whether to encode the task input or to skip the task input, and provide the respective decision of each task input to the task neural network. The task neural network is configured to: receive the sequence of task inputs, receive, from the RL neural network, for each task input of the sequence of task inputs, a respective decision that determines whether to encode the task input or to skip the task input, process each of the un-skipped task inputs in the sequence of task inputs to generate a respective accumulated feature for the un-skipped task input, wherein the respective accumulated feature characterizes features of the un-skipped task input and of previous un-skipped task inputs in the sequence, and generate a machine learning task output for the machine learning task based on the last accumulated feature generated for the last un-skipped task input in the sequence.