Saliency Prediction of Sports Videos: A Large-Scale Database and a Self-Adaptive Approach
Bibliographic record
Abstract
Predicting video saliency is crucial for improving sports video processing efficiency, thereby providing an enriched viewing experience for a wide-ranging audience. However, there is a long-term absence of well-established eye-tracking database and learning-based approach, particularly tailored for sports videos. In this paper, we establish a large-scale eye-tracking database dubbed audio-visual sports (AVS). AVS consists of 1,000 high-quality sports videos with eye fixations from 60 participants. Through the data analysis on AVS, we observe that human attention patterns exhibit significant variations based on the specific scene context of the sports. Motivated by this, we propose a sport-aware audiovisual saliency model, which can adaptively learn the scene context in a hyper manner. Specifically, a new audio-visual fusion (AVF) block is developed to effectively fuse features from the visual and audio backbone. After that, a hyper network is introduced to learn sport-aware priors, which are then adopted to guide the self-adaptive saliency predictor for predicting saliency map. Experimental results demonstrate that our approach outperforms other state-of-the-art saliency prediction models over the only two sports video eye-tracking databases.
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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 | 0.000 |
| 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 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".