DCFusion: Dual-Headed Fusion Strategy and Contextual Information Awareness for Infrared and Visible Remote Sensing Image
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
In remote sensing, the fusion of infrared and visible images is one of the common means of data processing. Its aim is to synthesize one fused image with abundant common and differential information from the source images. At present, the fusion methods based on deep learning are widely employed in this work. However, the existing fusion network with deep learning fails to effectively integrate common and differential information for source images. To alleviate the problem, we propose a dual-head fusion strategy and contextual information awareness fusion network (DCFusion) to preserve more meaningful information from source images. Firstly, we extract multi-scale features for the source images with multiple convolution and pooling layers. Then, we propose a dual-headed fusion strategy (DHFS) to fuse different modal features from the encoder. The DHFS can effectively preserve common and differential information for different modal features. Finally, we propose a contextual information awareness module (CIAM) to reconstruct the fused image. The CIAM can adequately exchange information from different scale features and improve fusion performance. Furthermore, the whole network was tested on MSRS and TNO datasets. The results of extensive experiments prove that our proposed network achieves good performance in target maintenance and texture preservation for fusion images.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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