Hallucinating Saliency Maps for Fine-Grained Image Classification for\n Limited Data Domains
Bibliographic record
Abstract
Most of the saliency methods are evaluated on their ability to generate\nsaliency maps, and not on their functionality in a complete vision pipeline,\nlike for instance, image classification. In the current paper, we propose an\napproach which does not require explicit saliency maps to improve image\nclassification, but they are learned implicitely, during the training of an\nend-to-end image classification task. We show that our approach obtains similar\nresults as the case when the saliency maps are provided explicitely. Combining\nRGB data with saliency maps represents a significant advantage for object\nrecognition, especially for the case when training data is limited. We validate\nour method on several datasets for fine-grained classification tasks (Flowers,\nBirds and Cars). In addition, we show that our saliency estimation method,\nwhich is trained without any saliency groundtruth data, obtains competitive\nresults on real image saliency benchmark (Toronto), and outperforms deep\nsaliency models with synthetic images (SID4VAM).\n
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".