Multimodal Deep Homography Estimation Using a Domain Adaptation Generative Adversarial Network
Bibliographic record
Abstract
Multimodal image registration is a challenging task. To begin with, the variation of parallax in the images makes the process intrinsically tricky. Additionally, due to phenomenology differences in modalities, the appearance of the same feature may vary significantly between the images making the registration laborious. To help mitigate these issues, we propose a two-step approach targeted at visible and infrared imagery. First, we train a generative adversarial network to learn the domain transfer function between the visible and the infrared domain, thereby mitigating the impact of the visual dissimilarity between the images. Second, we train a deep Siamese network to compute a homography in an unsupervised setting. Both elements are combined and trained sequentially. Our method is evaluated on a publicly available dataset. Our results show that the proposed method provides a reduction of more than 30% on average from the previous state-of-the-art, and outperforms several baselines and recent deep homography methods.
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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) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".