# Parameter-Dynamic Adaptive Fusion and Calibration Network for RGBT Tracking

arXiv: https://arxiv.org/abs/2608.01807 · HTML: https://mlsanity.com/paper/2608.01807/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [RGB-T Object Tracking:Benchmark and Baseline](https://mlsanity.com/paper/1805.08982/) (2018)
- [LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking](https://mlsanity.com/paper/2104.13202/) (2021)
- [Text-to-LoRA: Instant Transformer Adaption](https://mlsanity.com/paper/2506.06105/) (2025)
- [Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking](https://mlsanity.com/paper/2412.15691/) (2024)
- [SUTrack: Towards Simple and Unified Single Object Tracking](https://mlsanity.com/paper/2412.19138/) (2024)
- [XTrack: Multimodal Training Boosts RGB-X Video Object Trackers](https://mlsanity.com/paper/2405.17773/) (2024)
- [RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba](https://mlsanity.com/paper/2408.08827/) (2024)
- [HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling](https://mlsanity.com/paper/2403.13319/) (2024)
- [Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation](https://mlsanity.com/paper/2410.11586/) (2024)
- [CADTrack: Learning Contextual Aggregation with Deformable Alignment for Robust RGBT Tracking](https://mlsanity.com/paper/2511.17967/) (2025)
- [Unified Single-Stage Transformer Network for Efficient RGB-T Tracking](https://mlsanity.com/paper/2308.13764/) (2023)
- [How to Design and Train Your Implicit Neural Representation for Video Compression](https://mlsanity.com/paper/2506.24127/) (2025)
- [Dynamic Disentangled Fusion Network for RGBT Tracking](https://mlsanity.com/paper/2412.08441/) (2024)
- [Spatio-Temporal Conditional Denoising Transformer for Modality-Missing RGBT Tracking](https://mlsanity.com/paper/2607.24701/) (2026)

## Abstract (arXiv)

Existing RGBT trackers typically employ fusion functions with fixed parameters across different targets and scenarios. Although dynamic-architecture methods improve fusion flexibility by selecting among predefined operations, they still cannot adapt the fusion parameters to the evolving target state. To address these issues, we propose a Parameter-Dynamic Adaptive Fusion and Calibration Network (PAFCNet) for RGBT tracking. PAFCNet dynamically generates target-conditioned parameters for multimodal fusion and temporal calibration, enabling the tracking process to adapt to target appearance variations and modality quality fluctuations. Specifically, we introduce a Target-Adaptive Hypernetwork (TA-HyperNet) that leverages template representations, which preserve stable target identity and recent appearance changes with less background interference, to generate target-conditioned parameters for subsequent fusion and calibration. Based on TA-HyperNet, we design a target-aware parameter-dynamic fusion module that uses the generated parameters to modulate the fusion process. This enables the fusion module to adapt to changes in target appearance and complex scene conditions. Furthermore, since spatio-temporal information propagation may accumulate tracking noise, we propose a dynamic spatio-temporal calibration module that employs TA-HyperNet to generate calibration parameters for spatio-temporal tokens. By dynamically calibrating historical information before propagation, the module improves the reliability of temporal representations. Experimental results demonstrate that PAFCNet achieves competitive performance on multiple RGBT tracking benchmarks.