Tagging Driven by Interactive Image Discovery: Tagging-Tracking-Learning
Bibliographic record
Abstract
With the exponential growth of web image data, image tagging is becoming crucial in many applications such as e-commerce. However, despite the great progress achieved in various image tagging technologies, none of them are able to incorporate browsing and discovery activities on web viewers in such a way that a user can easily query an image and ask the question "what is that in the image?". We have developed a comprehensive online image tagging system based on a Tagging-Tracking-Learning (TTL) framework to solve this problem. Tagging images using this system is able to turn common static web images into non-intrusive interactive images. The system tracks all browsing and interaction activities of users over time to filter out low quality tags and in turn helps the tagging process by alleviating manual operations. In this paper, we describe the implementation of the TTL framework and the novel algorithms developed. Usability studies of the system indicate that the TTL framework provides a better user experiences and simplifies the process of obtaining large tagged image collections over state-of-the-art approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| 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".