Bibliographic record
Abstract
It has been shown that incorporation of human-specified high-level description of the target objects, e.g. labeled prior-knowledge data, can increase the performance of one-shot recognition. In this paper, we introduce latent components as a high level representation of the original objects and propose a cascade model for one-shot image recognition based on latent components learned by Hierarchical Dirichlet Process (HDP). In the proposed approach, instead of solving an optimization problem in the training stage, the latent high-level components are learned efficiently in a unsupervised way from unlabeled prior-knowledge data. Motivated by the facts that HDP is an infinite mixture model proposed in the literature for document modeling that can infer the unknown mixture components and the number of components from the data, and that bag-of-feature model is a standard representation in document retrieval and computer vision areas, we adopt HDP model to infer the mixture components (like latent topics in documents) for target images from unlabeled image visual word vocabulary, and we then train a classifier to associate the components with class labels. The superior performances of the proposed one-shot recognition method are illustrated by testing the Caltech category dataset and the " Animals with Attributes" dataset.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".