Label Importance Ranking with Entropy Variation Complex Networks for Structured Video Captioning
Bibliographic record
Abstract
Structured video captioning is a fundamental yet challenging task in both computer vision and artificial intelligence (AI).The prevalent approach is to map an input video to a variablelength output sentence with models like recurrent neural network (RNN).This paper presents a new model based on an improved scene-aware bidirectional long short-term memory network (SABi-LSTM), and names the model as label importance ranking with entropy variation complex networks of structured video captions.Structured video captioning is a three-level structured system, including a multi-feature fusion level, an SABi-LSTM level, and a label importance ranking level.The system decomposes structures of multiple levels and dimensions from different perspectives to perform video captioning.This work affirms the theoretical and practical significance of label importance ranking to video caption generation, and regards entropy as a local level metric to quantify label importance.Hence, entropy variation was proposed to define label importance, namely, the variation of the network entropy through label removal.It is assumed that the removal of an important label could cause sustainable variation to the structure.Hence, the authors defined the label importance ranking with entropy variation complex network algorithm to calculate the weight model of label nodes marked by video, and obtain the final caption of the video.Empirical results on Microsoft Video Caption (MSVD) dataset and MSR-Video to Text (MSR-VTT) dataset demonstrate the superiority of our approach for structured video captioning, especially on MSVD dataset.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".