Deep Variation Transformation Network for Foreground Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In existing literature, the distribution of pixel observations is analyzed with models designed for the video foreground detection task. However, it is possible that the background and foreground share similar observations, causing false detections. We propose a novel foreground detection method called Deep Variation Transformation Network (DVTN), focusing on analyzing the pixel variations instead of distributions. In particular, pixel variations are represented by a sequence of pixel observations, and DVTN is trained to transform the pixel variations into a new space, where the observations can be classified easily. Following this, the output of DVTN is utilized by a linear classifier to label pixels as foreground or background. As a result of the global analysis and the strong learning ability of DVTN, the proposed approach adaptively learns a good transformation from pixel variations to probabilities of labels to improve performance. Comprehensive experiments on several benchmark datasets demonstrate the superiority of our DVTN approach compared to both state-of-the-art deep learning and traditional methods, especially in scenes lacking texture and color information. Code is available at https://github.com/Zhangjunyin/DVTN.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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