Cloud based content classification with global-connected net (GC-Net)
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".