"Flobject" Analysis: Learning about Static Images from Motion
Why this work is in the frame
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Bibliographic record
Abstract
A critical practical problem in the field of object recognition is an insufficient number of labeled training images, as manually labeling images is a time consuming task. For this reason, unsupervised learning techniques are used to take advantage of unlabeled training images to extract image representations that are useful for classification. However, unsupervised learning is in general difficult. We propose simplifying the unsupervised training problem considerably by taking the advance of motion information. The output of our method is a model that can generate a vector representation from any static image. However, the model is trained using images with additional motion information. To demonstrate the flobject analysis framework, we extend the latent Dirichlet allocation model to account for word-specific flow vectors. We show that the static image representations extracted using our model achieve higher classification rates and better generalization than standard topic models, spatial pyramid matching, and Gist descriptors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it