Hidden-concept driven image decomposition towards semi-supervised multi-label image annotation
Bibliographic record
Abstract
Conventional semi-supervised learning algorithms over multi-label image data propagate labels predominantly via the holistic image similarities, ignoring that each label essentially only characterizes a local region within an image. In this paper, we present a novel propagation-by-decomposition solution to this problem with the following characteristics: 1) each image representation is implicitly decomposed into several label representations; 2) those decompositions are guided by the so-called hidden concepts, which are expected to characterize image regions and be able to reconstruct both visual and non-visual labels of the entire image label space; 3) the intra-label diversity is expressed by the hidden-concept-specific subspace, which acts as the intermediate entity for propagating specific label from labeled data to unlabeled ones; and 4) the sparse coding based graph is proposed to enforce the collective consistency between image labels and image representations, which naturally avoids the dilemma of possible inconsistency between the pairwise label similarity and image representation similarity in multi-label scenario. These properties are finally embodied in a regularized nonnegative data factorization formulation, from which a convergence provable updating procedure is presented to iteratively optimize the objective function. Extensive experiments on three benchmark image datasets well validate the effectiveness of our proposed solution to semi-supervised multi-label image annotation problem.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".