VideoMAE Temporal Progressive Learning for Reduced Training Cost
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Solution Overview
Problem
Training video masked autoencoders (MAE) is slow and costly, requiring significant computational resources and time, making them inaccessible to users with limited budgets and limiting scalability to larger datasets.
Innovation Solution
Implement a temporal progressive learning (TPL) strategy that decomposes videoMAE training into smaller sub-tasks, starting with single-frame spatial semantics learning and progressively increasing clip temporal length in multiple stages, optimizing parameters like batch size and learning rate to reduce computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional videoMAE training is performed, then model performance is achieved, but training time and computational cost increase significantly
Solution Approach 1:
The training process is segmented into multiple stages, where each stage processes a different number of frames. Stage 1 processes 1 frame, Stage 2 processes 2 frames, Stage 3 processes 4 frames, and Stage 4 processes 8 frames. This segmentation allows the model to learn progressively from simpler to more complex temporal patterns, reducing overall training time while maintaining performance.
Solution Approach 2:
The method performs preliminary training on smaller subsets of frames before progressing to full-frame training. By pre-training the model on 1-frame and 2-frame sequences first, the model develops foundational spatial and temporal understanding that accelerates subsequent training on longer sequences, thereby reducing total training time.
2Measurement precision
If conventional videoMAE training is performed, then model performance is achieved, but computational resources and cost increase
Solution Approach 1:
Computational resources are segmented across four training stages with increasing frame counts. Each stage uses progressively more computational resources, allowing the system to allocate resources efficiently rather than requiring full computational power from the start. This reduces peak computational cost while achieving the same performance.
Solution Approach 2:
The method uses partial action by training on subsets of frames (1, 2, 4, or 8 frames) rather than always processing all frames simultaneously. This partial processing reduces computational cost at each stage while the cumulative effect across stages achieves full training performance.
3Measurement precision
If conventional videoMAE training is performed, then model capability is achieved, but scalability to larger datasets is limited
Solution Approach 1:
The training framework is dynamic, allowing users to select different frame counts per stage based on available resources and dataset size. The multi-stage architecture can be adapted to process varying amounts of data efficiently, making the system scalable from small to large datasets without requiring complete reconfiguration.
Data Source
AI summary
Systems and methods for performing temporal progressive learning for video processing are provided herein. Some examples include receiving a video that includes a plurality of frames, extracting a first subset of frames from the plurality of frames, and inputting the first subset of frames into a model that includes an encoder and a decoder. The examples further include comparing a first output of the model to the first subset of frames and updating the encoder, thereby training the encoder, and extracting a second subset of frames from the plurality of frames. The second subset of frames includes a number of frames that is larger than a number of frames in the first subset of frames. The examples further include inputting the second subset of frames into the model, comparing a second output of the model to the second subset of frames and updating the encoder, thereby further training the encoder.