Learnable Laplacian Embedding Decomposition for One-Stream Visual Object Trackers
Bibliographic record
Abstract
Recent advancements in the one-stream tracking framework have demonstrated the power of unified feature learning and relation modelling for robust visual object tracking. The methods under this framework might suffer from noisy attention scores in the process of extraction of the feature maps within the transformer blocks. This paper introduces a novel Learnable Laplacian Embedding Decomposition (LLED) module designed to enhance one-stream trackers by explicitly decomposing the learned feature embeddings into a set of Laplacian embeddings, each representing a different scale of structural information that works as a band-pass filter after each encoder block. This filter rejects attention scores corresponding to extremely low or high frequencies, effectively suppressing noise and background distractors while emphasizing features most relevant to the target object. Experimental results on multiple challenging benchmarks, including GOT-10k, UAV123, and OTB2015, demonstrate that LLED, which is enhanced by the proposed band-pass filtering, significantly improves the localization accuracy of state-of-the-art one-stream trackers while maintaining their real-time speed. This work highlights the importance of structural information and its interplay with attentional mechanisms for precise tracking and provides a novel approach for integrating it into the efficient one-stream paradigm.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".