STTATrack: Enhancing One-Stream Single Object Tracking via Score Temporal Token Attention
Bibliographic record
Abstract
One-stream transformer-based trackers have shown remarkable success in single object tracking by jointly performing feature extraction and relation modeling. However, the update of temporal context, often propagated via temporal tokens, typically relies on general self-attention mechanisms within the transformer backbone. This paper introduces STTATrack, a novel framework that enhances one-stream tracking by explicitly leveraging the immediate spatial certainty from the current frame’s prediction score map to refine these propagated temporal query tokens. Our core contribution, the Score Temporal Token Attention (STTA) module, generates an embedding from the score map and employs a dual attention mechanism to facilitate bidirectional information flow between this spatial certainty embedding and the existing temporal tokens. This targeted refinement allows temporal tokens to be dynamically adapted based on the most current and spatially precise evidence, leading to improved adaptability and temporal consistency. STTATrack builds upon the ODTrack architecture and demonstrates significant performance improvements on the challenging GOT10k, OTB and UAV123 benchmarks, underscoring the efficacy of explicitly integrating current-frame spatial certainty into the temporal refinement loop.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".