Deep Reinforcement Learning with Parameterized Action Space for Object Detection
Why this work is in the frame
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Bibliographic record
Abstract
Object detection is a fundamental task in computer vision. With the remarkable progress made in big visual data analytics and deep learning, Reinforcement Learning (RL) is becoming a promising framework to model the object detection problem since the detection procedure can be cast as a Markov decision process (MDP). We propose a Reinforcement Learning system with parameterized action space for image object detection. The proposed system uses an active agent exploring in a scene to identify the location of a target object, and learns a policy to refine the geometry of the agent by taking simple actions in parameterized space, which integrates the discrete actions and its corresponding continuous parameters. We then optimize the representation of the generated region proposals with the discriminative multiple canonical correlation analysis (DMCCA) [11] in preparation for classification with Fast R-CNN. Experiments on PASCAL VOC 2007 and 2012 datasets show the effectiveness of the proposed method.
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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