An Unsupervised Hierarchical Feature Learning Framework for One-Shot Image Recognition
Bibliographic record
Abstract
One-shot recognition has attracted increasing attention recently, inspired by the fact that human cognitive systems could perform recognition tasks well provided only one or a few labeled training samples, in contrast to the conventional object recognition systems that require a large number of labeled training images. One-shot recognition is a visual classification task, where only one training sample is available for each object category in the target test domain, with the help of prior-knowledge data from the source domain. In this paper, we tackle this challenging one-shot recognition problem under a more exciting setting by using only unlabeled images as prior knowledge, which requires less labeling effort than previous works which adopt fully labeled data and/or a sophisticated attribute table designed by human experts. We propose a novel unsupervised hierarchical feature learning framework to learn a feature pyramid from the prior-knowledge domain. The proposed feature learning method also could be applied across multiple feature spaces. Furthermore, we propose using pyramid-matching kernels to combine multilevel features. Examining the “Animals with Attributes” and Caltech-4 data sets in our one-shot recognition setting, we show that the proposed unsupervised feature learning approach with very limited information could achieve comparable performance to that of supervised ones.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".