Boosting Prediction of Geo-location for Web Images Through Integrating Multiple Knowledge Sources
Bibliographic record
Abstract
Estimating geographical information of a given photo is a challenging task due to the massive spread of candidate locations on the earth. With the help of freely available geo-tagged Web images, the problem can be addressed by propagating geo-coordinates (latitude and longitude) of geo-related training data, which is obtained using document retrieval techniques. The state-of-the-art approach adopts language modeling technique to estimate the probability distribution of image associated tags in a local region. Under this framework, we propose to differentiate the tags based on the knowledge explored from multiple sources. Finally, a set of geo-informative tags are identified and further emphasized during the model learning and geo-location prediction. In addition, accurate geo-coordinates are estimated by incorporating the image visual information. Experiments on a large-scale geo-tagged Flickr image dataset demonstrate the effectiveness of proposed method at different levels of evaluation granularity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".