Learning Domain-Adaptive Landmark Detection-Based Self-Supervised Video Synchronization for Remote Sensing Panorama
Bibliographic record
Abstract
The synchronization of videos is an essential pre-processing step for multi-view reconstruction such as the image mosaic by UAV remote sensing; it is often solved with hardware solutions in motion capture studios. However, traditional synchronization setups rely on manual interventions or software solutions and only fit for a particular domain of motions. In this paper, we propose a self-supervised video synchronization algorithm that attains high accuracy in diverse scenarios without cumbersome manual intervention. At the core is a motion-based video synchronization algorithm that infers temporal offsets from the trajectories of moving objects in the videos. It is complemented by a self-supervised scene decomposition algorithm that detects common parts and their motion tracks in two or more videos, without requiring any manual positional supervision. We evaluate our approach on three different datasets, including the motion of humans, animals, and simulated objects, and use it to build the view panorama of the remote sensing field. All experiments demonstrate that the proposed location-based synchronization is more effective compared to the state-of-the-art methods, and our self-supervised inference approaches the accuracy of supervised solutions, while being much easier to adapt to a new target domain.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".