# Dense Feature Aggregation and Pruning for RGBT Tracking

arXiv: https://arxiv.org/abs/1907.10451 · HTML: https://mlsanity.com/paper/1907.10451/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 30 of 40)

- [Visual Prompt Multi-Modal Tracking](https://mlsanity.com/paper/2303.10826/) (2023)
- [LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking](https://mlsanity.com/paper/2104.13202/) (2021)
- [Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2204.04120/) (2022)
- [Object Tracking by Jointly Exploiting Frame and Event Domain](https://mlsanity.com/paper/2109.09052/) (2021)
- [OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning](https://mlsanity.com/paper/2403.09634/) (2024)
- [Bi-directional Adapter for Multi-modal Tracking](https://mlsanity.com/paper/2312.10611/) (2023)
- [Generative-based Fusion Mechanism for Multi-Modal Tracking](https://mlsanity.com/paper/2309.01728/) (2023)
- [MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking](https://mlsanity.com/paper/2107.10433/) (2021)
- [Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking](https://mlsanity.com/paper/2201.08673/) (2022)
- [Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2304.14394/) (2023)
- [What You Have is What You Track: Adaptive and Robust Multimodal Tracking](https://mlsanity.com/paper/2507.05899/) (2025)
- [Cross-modulated Attention Transformer for RGBT Tracking](https://mlsanity.com/paper/2408.02222/) (2024)
- [Siamese Infrared and Visible Light Fusion Network for RGB-T Tracking](https://mlsanity.com/paper/2103.07302/) (2021)
- [Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens](https://mlsanity.com/paper/2401.01674/) (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)
- [CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features](https://mlsanity.com/paper/2505.19434/) (2025)
- [RGB-T Tracking via Multi-Modal Mutual Prompt Learning](https://mlsanity.com/paper/2308.16386/) (2023)
- [RGB-T Tracking Based on Mixed Attention](https://mlsanity.com/paper/2304.04264/) (2023)
- [AFter: Attention-based Fusion Router for RGBT Tracking](https://mlsanity.com/paper/2405.02717/) (2024)
- [Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking](https://mlsanity.com/paper/2108.04521/) (2021)
- [Temporal Aggregation for Adaptive RGBT Tracking](https://mlsanity.com/paper/2201.08949/) (2022)
- [A Survey for Deep RGBT Tracking](https://mlsanity.com/paper/2201.09296/) (2022)
- [Unified Single-Stage Transformer Network for Efficient RGB-T Tracking](https://mlsanity.com/paper/2308.13764/) (2023)
- [RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning](https://mlsanity.com/paper/2303.14778/) (2023)
- [Revisiting Outage for Edge Inference Systems](https://mlsanity.com/paper/2504.03686/) (2025)
- [Single Object Tracking Research: A Survey](https://mlsanity.com/paper/2204.11410/) (2022)
- [X Modality Assisting RGBT Object Tracking](https://mlsanity.com/paper/2312.17273/) (2023)
- [From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation](https://mlsanity.com/paper/2403.16834/) (2024)
- [Breaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking](https://mlsanity.com/paper/2503.11247/) (2025)

## Abstract (arXiv)

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods.