Accelerated Feature Extraction and Refinement for Improved Aerial Scene Categorization
Bibliographic record
Abstract
Deep learning has displayed superior performance in aerial scene (AS) categorization. However, existing methods for AS classification tend to lack adaptability and efficiency, particularly in optimizing its performance on different embedded devices. They also often fail to dynamically adjust to varying scales of feature representations, which can limit their effectiveness across different datasets and devices. To solve these issues, we provide two algorithms. The first algorithm explores local key features by mining the interactivity between channels. The range of cross-channel interactions is dynamically determined through an adaptive strategy. This ensures that convolution operations are optimized. The resulting features are improved by the second proposed algorithm. The second algorithm introduces convolutions, attention, and functions to calculate the weight of features. It enhances feature discriminative power by assigning adaptive weights. Then, we introduce an inference acceleration method for AS categorization. We create efficient codes for the proposed algorithms through automated optimization to match different devices. The inference time of the proposed method is reduced on various devices. Experiments on three frequently used datasets show we attain higher accuracy. The accuracy of some categories reaches 100%. In contrast to similar methods, our accelerated algorithm has at least 43.16%, 53.37%, 10.07%, 31.16%, and 8.08% less inference time on RTX 2,080Ti, Titan V, Jetson TX2, Jetson NX, and Jetson Nano GPUs, respectively.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".