Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks
Bibliographic record
Abstract
We propose a probabilistic graphical framework for multi-instance learning (MIL) based on Markov networks. This framework can deal with different levels of labeling ambiguity (i.e., the portion of positive instances in a bag) in weakly supervised data by parameterizing cardinality potential functions. Consequently, it can be used to encode different cardinality-based multi-instance assumptions, ranging from the standard MIL assumption to more general assumptions. In addition, this framework can be efficiently used for both binary and multiclass classification. To this end, an efficient inference algorithm and a discriminative latent max-margin learning algorithm are introduced to train and test the proposed multi-instance Markov network models. We evaluate the performance of the proposed framework on binary and multi-class MIL benchmark datasets as well as two challenging computer vision tasks: cyclist helmet recognition and human group activity recognition. Experimental results verify that encoding the degree of ambiguity in data can improve classification performance.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".