Bibliographic record
Abstract
In this paper, we define the freestyle object localization problem where a model is expected to autonomically learn and locate an arbitrarily specified object given only the name. However, learning the semantic features of an object requires corresponding training data which are not always available in existing datasets. Obviously, Internet provides massive off-the-rack data for learning given only the keyword while few existing methods are able to directly use them. To directly use them, a localization model should firstly overcome two difficulties: (1) learning from single-class annotated images and (2) from images with unpredictable sizes. For single-class annotation, we establish a novel probabilistic model to capture the distribution of the object in the background data space. Based on a deep hierarchical architecture with alterable depth that is designed for unpredictable sizes, we propose an association module and design an energy function to drive the probabilistic model. By maximizing the log-likelihood, the proposed model is able to highlight the semantic features of object that has the highest probability in training images, i.e., the specified object. In experiments, the proposed model achieves state-of-the-art performance among weakly-supervised localization methods on public datasets and freestyle localization of objects that are rare in public datasets.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".