Unsupervised Global Difference Modeling for Image Change Detection
Bibliographic record
Abstract
In change detection, impact of non-intrinsic changes such as those caused by illumination, season, and viewing angle variances are common in practice but also a great challenge for change detection methods. In this paper, we propose a novel unsupervised image change detection method by modeling global difference information to deal with such non-intrinsic changes. Comparing global features can mitigate the impact of them due to the global consistency of them in the same scene at the same time. But global modeling for change detection also faces the challenges of feature learning with limited data and difficulty in generating pixel-wise changed regions. To overcome the challenges, firstly, we use a backbone network to capture the global features of bi-temporal images. Then an energy function is designed with a masked difference between the two features and a margin-aware constraint in order to align the global features and meanwhile maintain detail information. To train the network with only two images, we propose an adversarial learning method by introducing a generalization network that consecutively generates two images that can minimize the energy. Then a new loss function is derived to alternately train the feature learning network and generalization network. Secondly, after learning with bi-temporal images, it is also important to generate the pixel-wise changed regions. Then we design a difference mapping method that maps the changed regions from global difference. Experiments on different types of data by comparing with both supervised and unsupervised methods demonstrate the effectiveness of the proposed method
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".