Unifying Top–Down Views by Task-Specific Domain Adaptation
Bibliographic record
Abstract
In this article, we aim to learn a unified representation of images from satellite/aerial/ground views by exploring their underlying correlations. Inspired by recent advances in domain adaptation (DA), we propose a novel task-specific DA method for this purpose. Different from traditional DA methods, this proposed method not only applies task-specific classifiers1but also introduces domain-specific tasks for different domains during the adaptation process. The experiments are conducted on two newly proposed ground-/satellite-to-aerial scene adaptation (GSSA) data sets. Since the semantic gap between the ground/satellite scenes and the aerial scenes is much larger than that between ground scenes, the DA task between these scenes is more challenging than traditional DA tasks. On GSSA data sets, we not only demonstrate the proposed unsupervised DA method but also explore the few-shot DA in the discussion section. The proposed method is easy to implement, and our method substantially outperforms the state-of-the-art methods on the studied data sets.We hope that the proposed method for the novel GSSA data sets can be a good baseline for future researchers. The related data sets/codes will be available online.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".