Bibliographic record
Abstract
Video forecasting is an emerging topic in the computer vision field, and it is a pivotal step toward unsupervised video understanding. However, the predictions generated from the state-of-the-art methods might be far from ideal quality, due to a lack of guidance from the labeled data of correct predictions (e.g., the annotated future pose of a person). Hence, building a network for better predicting future sequences in an unsupervised manner has to be further pursued. To this end, we put forth a novel Forward-Backward-Net (FB-Net) architecture, which delves deeper into spatiotemporal consistency. It first derives the forward consistency from the raw historical observations. In contrast to mainstream video forecasting approaches, FB-Net then investigates the backward consistency from the future to the past to reinforce the predictions. The final predicted results are inferred by jointly taking both the forward and backward consistencies into account. Moreover, we embed the motion dynamics and the visual content into a single framework via the FB-Net architecture, which significantly differs from learning each component throughout the videos separately. We evaluate our FB-Net on the large-scale KTH and UCF101 datasets. The experiments show that it can introduce considerable margin improvements with respect to most recent leading studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".