Dynamic Adaptation of Activation Function to Fine Tune Video ResNet for Fight or Non-Fight Classification
Bibliographic record
Abstract
The task of designing and training a 3D convolutional neural network (CNN) from scratch poses significant complexity, necessitating high levels of expertise to achieve a performance that rivals the state-of-the-art.To circumvent this, fine-tuning of neural networks has emerged as a formidable approach.This study focuses on the utilization of Video ResNet, a state-of-the-art architecture known for its proficiency in capturing spatiotemporal patterns from video data.A novel approach is proposed for the fine-tuning of the 3D CNN model (Video ResNet) that involves altering activation functions over epochs while maintaining the network weights and biases consistent.This dynamic approach was assessed under various hyperparameters, yielding encouraging results.Contrary to most studies that employ down-sampling of the temporal sequence to minimize memory requirements, this study introduces a sliding window-based approach to evade down-sampling and prevent potential information loss.The proposed methodology yielded an accuracy of 87.25% in the fight/non-fight classification on the RWF-2000 dataset, marginally surpassing the performance of the state-of-the-art model.The proposed method not only facilitates the development of a real-time video incident detection model but also addresses the issue of overfitting during training through the incorporation of adaptive dynamic activation functions.This study thus contributes to the ongoing advancements in the field of neural network fine-tuning and video data classification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".