CLT-Det: Correlation Learning Based on Transformer for Detecting Dense Objects in Remote Sensing Images
Why this work is in the frame
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Bibliographic record
Abstract
Challenges still exist in the task of object detection in remote sensing images with densely distributed objects due to large variation in scale and neglect of the relative position and correlation. To address these issues, a Correlation Learning Detector based on Transformer (CLT-Det) is proposed for detecting dense objects in remote sensing images. A Transformer Attention Module (TAM) is designed to improve the densely packed objects’ model representation ability by learning pixel-wise attention with Transformer. To alleviate the semantic gap caused by variations in scale, a Feature Refinement Module (FRM) is proposed by improving the multi-scale feature pyramid. A Correlation Transformer Module (CTM) is proposed to extract correlation information and encodes position information of dense objects’ features on the classification branch for fully utilizing the position information and correlation among objects. Extensive experiments compared with several state-of-art methods on two challenging remote sensing datasets, namely DOTA and HRSC2016, demonstrate that the proposed CLT-Det achieves promising and competitive performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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