EARB: An Edge-Assisted Residual Block for Image Retrieval
Bibliographic record
Abstract
Deep convolutional neural networks have revolutionized many computer vision tasks, including image retrieval. The most crucial factor for using convolutional neural networks in image retrieval is their ability to extract highly representative features from images. Among existing deep networks, residual networks demonstrate higher performance in the task of image retrieval. In this paper, a new residual block is proposed to generate rich sets of edge-assisted features for image retrieval. The proposed residual block comprises three modules: the edge feature extraction module, the hierarchical feature extraction module, and the feature fusion module. Fusing the conventional features provided by the hierarchical feature extraction module and those of the features obtained from the edge feature extraction module enables the network to learn very rich feature sets. This, in turn, increases the representational ability of the obtained feature sets for image retrieval. The deep image retrieval network utilizing the proposed block is evaluated on various benchmark datasets. Experimental results confirm that utilization of the proposed residual block in a deep image retrieval network exhibits superior retrieval performance to that provided by other image retrieval networks.
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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 | 0.001 | 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 | 0.001 | 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".