Feature Fusion and Enhancement for Lightweight Visible-Thermal Infrared Tracking via Multiple Adapters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visible light and thermal infrared tracking combines the characteristics of visible light and thermal infrared modalities to achieve robust target tracking in all-weather and all-day scenarios. However, most existing visible light and thermal infrared tracking methods rely on either full fine-tuning or attention mechanisms, which introduce a large number of parameters and are predominantly influenced by the visible modality. This results in challenges such as high computational complexity, slower processing speeds, and limited exploitation of multimodal information. To address these issues, this paper proposes a lightweight multimodal tracking model based on feature fusion and enhancement. The model consists of a feature fusion adapter and a joint enhancement adapter, designed to integrate and refine information across modalities. It employs a dual-stream transformer encoder with shared parameters across modality branches, utilizing a frozen pre-trained foundation model to independently extract features from visible light and thermal infrared inputs. The lightweight fusion adapter combines modality-specific information, while the joint enhancement adapter refines unimodal features, introducing only 0.23M trainable parameters. Experimental results on the LasHeR benchmark demonstrate that the proposed method outperforms prompt learning and other adapter-based methods, achieving a 4.4% improvement in PR and a 3.3% increase in SR while maintaining computational efficiency. With a real-time inference speed of 28.60 FPS, the proposed method balances accuracy and efficiency effectively. The source code will be available at https://github.com/huxue/MFJA.
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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 | 0.001 | 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 it