Cloud based content classification with global-connected net (GC-Net)
Why this work is in the frame
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Bibliographic record
Abstract
Real-time classification of video content has been envisioned to revolutionize human lives. The paper introduces a cloud-based video classification system that is able to perform lightweight video classification on real-time captured video content. An Internet of things (IoT) device, called Content Classification Box (CCB), is defined as an add-on to one or a number of cameras in vicinity for content classification. The CCB will communicate with the cloud server once any interested content/event (such as abnormality) is identified, in which the corresponding video content is transported to the cloud server for further inspection. To achieve the lightweight and intelligent video content classification at the CCB, a novel convolutional neural network (CNN) framework, namely Global-Connected Net (GC-Net), is introduced. GC-Net is featured by a novel deep learning architecture for exploitation of all the earlier hidden layer neurons, as well as an activation function that has the potential to approximate complexity functions. We will show that the proposed CNN framework can achieve similar performance in a number of object recognition benchmark tasks, namely MNIST and CIFAR-10/100, under significantly less number of parameters, thus being able to apply to low-computation and low-memory scenarios.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it