3D Multi-Views Object Classification Based on a Fully Generalized Dirichlet Allocation Model
Bibliographic record
Abstract
Comparing 3D objects based on their local features necessitates a substantial amount of computational resources. Recent studies have demonstrated that the combination of topic modeling and the Bag of Visual Words (BoVWs) approach can effectively capture useful and distinguishable object rep-resentations in a consistent way. One of the most common topic modeling approaches is LDA which is based on Dirichlet distribution priors, but it is limited by its incapacity in modeling topic correlations. Consequently, several extensions of LDA were proposed to solve this problem including GD-LDA, LGDA, and CVB-LGDA which have shown good results in discovering the semantic relationships between topics but are either suffering from incomplete generative processes assumptions that impact their inference efficiency or require high-computational power due to their complexity. In this paper, we introduce F-GDA, a fully Generalized Dirichlet Allocation model that is mainly derived from Generalized Dirichlet distributions for 3D objects recognition. Unlike GD-LDA which generalizes only the topics parameter, F-GDA generalizes all the model priors parameters, ensuring complete flexibility in the priors. Extensive experimental results have demonstrated the ability of our model to learn high-quality data representations of a real-world 3D Multi-views dataset ETH80 and also N15.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".