Development of a Deep Image Retrieval Network Using Hierarchical and Multi-scale Spatial Features
Bibliographic record
Abstract
Image retrieval aims to find similar images to a given query by matching features extracted directly from the images of a database. Deep convolutional neural networks provide an excellent framework for obtaining highly representative feature vectors from images to improve an image retrieval method. Deep residual networks perform better than existing deep networks, as they can incorporate useful information into the feature vectors through residual learning by designing appropriate operations in the employed residual block. One type of such information is spatial information obtained at different scales and levels of abstraction. In this paper, a novel residual block is proposed to generate a rich set of features for the task of image retrieval. The development of the residual block consists of three modules: a hierarchical spatial feature extraction module focusing on spatial information at different abstraction levels, a multi-scale feature extraction module that generates features at three different scales, and a feature fusion module. The results of experiments on various datasets and an ablation study show that the proposed residual block noticeably improves the representational capacity of the network, which, in turn, significantly enhances the retrieval performance of the deep image retrieval network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".