Pose-Aware Embedding Networks and Multi-Modal Image-Language Retrieval
Bibliographic record
Abstract
<p>Inspired by recent work in human pose metric learning this thesis explores a family of pose-aware embedding networks designed for the purpose of image similarity retrieval. Circumventing the need for direct human joint localization, a series of CNN embedding networks are trained to respect a variety of Euclidean and language-primitive metric spaces. Querying with imagery alone presents certain limitations and thus this thesis proposes a multi-modal image-language embedding space, extending the current model to allow for language-primitive queries. This additional language mode provides the benefit of improving retrieval quality by 3% to 14% under the hit@k metric. Finally, two approaches are constructed to address the issues of conducting partial language-primitive queries, with the former generating maximally likely descriptors and the latter exploiting the network’s tendency to factorize the embedding space into (mostly) linearly separable sub-spaces. These two approaches improve upon recall by 13% and 17% over the provided baselines.</p>
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".