Bibliographic record
Abstract
Existing approaches for semantic segmentation in videos usually extract each frame as an RGB image, then apply standard image-based semantic segmentation models on each frame. This is time-consuming. In this paper, we tackle this problem by exploring the nature of video compression techniques. A compressed video contains three types of frames, I-frames, P-frames, and B-frames. I-frames are represented as regular images, P-frames are represented as motion vectors and residual errors, and B-frames are bidirectionally frames that can be regarded as a special case of a P frame. We propose a method that directly operates on I-frames (as RGB images) and P-frames (motion vectors and residual errors) in a video. Our proposed model uses a ConvLSTM model to capture the temporal information in the video required for producing the semantic segmentation on P-frames. Our experimental results show that our method performs much faster than other alternatives while achieveing similar performance in terms of accuracies.
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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 | 0.000 | 0.001 |
| 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 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".