Learning Neural Networks with Ranking-Based Losses for Action Retrieval
Why this work is in the frame
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Bibliographic record
Abstract
We consider the problem of learning image/video retrieval using a neural network based approach that optimizes the ROC loss function. Neural network is one of the most widely used techniques in computer vision. Standard neural network uses simple loss functions, such as the softmax loss or hinge loss over labels. Such loss functions are suitable for standard classification problems where the performance is measured by the overall accuracy. For image/video retrieval, the performance is usually measured by some ranking-based loss that is not well captured by the softmax loss or hinge loss. In this paper, we develop a learning approach that incorporates the ranking-based loss function in neural network. We apply our approach in the problem of action retrieval in static images and videos. The experimental results show that our proposed approach outperforms standard neural networks trained with softmax loss as well as an SVM-based approach that also optimizes the ROC loss function.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it