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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".