A Lightweight Dual‐Branch Meta‐Learner for Few‐Shot HSI Classification With Cross‐Domain Adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Hyperspectral imaging (HSI) plays a crucial role in urban area analysis from satellite data and supports the continuous advancement of intelligent cities. However, its practical deployment is hindered by two major challenges: the scarcity of reliable training annotations and the high spectral similarity among different land‐cover classes. To address these issues, this paper introduces a novel meta‐learning framework that synergistically combines knowledge transfer across domains with a dual‐adjustment mode (comprising intracorrection (IC) and interalignment (IA)), while ensuring end‐to‐end trainability. Our contributions are twofold. (1) We refine the 3D attention network TGAN into TGAN2 (3D ghost attention network v2) by replacing the original ghost blocks with ghost‐V2 modules and enlarging the receptive field to capture global context. (2) We propose a dual‐adjustment mode (comprising intracorrection (IC) and interalignment (IA)) to generate robust class prototypes and mitigate domain shift. These innovations are integrated into our overarching framework, DMCM2 (dual‐adjustment cross‐domain meta‐learning framework v2), which is unified by its end‐to‐end trainability and efficiency. The code and models will be publicly available at: https://github.com/YAO‐JQ/DMCM2 .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it