# Duality-Gated Mutual Condition Network for RGBT Tracking

arXiv: https://arxiv.org/abs/2011.07188 · HTML: https://mlsanity.com/paper/2011.07188/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 30 of 35)

- [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)
- [Towards Universal Modal Tracking with Online Dense Temporal Token Learning](https://mlsanity.com/paper/2507.20177/) (2025)
- [Modality-missing RGBT Tracking: Invertible Prompt Learning and High-quality Benchmarks](https://mlsanity.com/paper/2312.16244/) (2023)
- [RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba](https://mlsanity.com/paper/2408.08827/) (2024)
- [Cross-modulated Attention Transformer for RGBT Tracking](https://mlsanity.com/paper/2408.02222/) (2024)
- [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)
- [RGB-T Tracking via Multi-Modal Mutual Prompt Learning](https://mlsanity.com/paper/2308.16386/) (2023)
- [Cross-Modal Object Tracking via Modality-Aware Fusion Network and A Large-Scale Dataset](https://mlsanity.com/paper/2312.14446/) (2023)
- [ICPL-ReID: Identity-Conditional Prompt Learning for Multi-Spectral Object Re-Identification](https://mlsanity.com/paper/2505.17821/) (2025)
- [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)
- [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)
- [Transformer-based RGB-T Tracking with Channel and Spatial Feature Fusion](https://mlsanity.com/paper/2405.03177/) (2024)
- [SeaDATE: Remedy Dual-Attention Transformer with Semantic Alignment via Contrast Learning for Multimodal Object Detection](https://mlsanity.com/paper/2410.11358/) (2024)
- [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)
- [Cross Fusion RGB-T Tracking with Bi-directional Adapter](https://mlsanity.com/paper/2408.16979/) (2024)
- [Breaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking](https://mlsanity.com/paper/2503.11247/) (2025)
- [Omni Survey for Multimodality Analysis in Visual Object Tracking](https://mlsanity.com/paper/2508.13000/) (2025)
- [MTNet: Learning modality-aware representation with transformer for RGBT tracking](https://mlsanity.com/paper/2508.17280/) (2025)
- [RAGTrack: Language-aware RGBT Tracking with Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.03617/) (2026)
- [Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking](https://mlsanity.com/paper/2403.18193/) (2024)
- [Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos](https://mlsanity.com/paper/2412.10861/) (2024)
- [BTMTrack: Robust RGB-T Tracking via Dual-template Bridging and Temporal-Modal Candidate Elimination](https://mlsanity.com/paper/2501.03616/) (2025)
- [Towards General Multimodal Visual Tracking](https://mlsanity.com/paper/2503.11218/) (2025)
- [Learning Frequency and Memory-Aware Prompts for Multi-Modal Object Tracking](https://mlsanity.com/paper/2506.23972/) (2025)
- [SwiTrack: Tri-State Switch for Cross-Modal Object Tracking](https://mlsanity.com/paper/2511.16227/) (2025)

## Abstract (arXiv)

Low-quality modalities contain not only a lot of noisy information but also some discriminative features in RGBT tracking. However, the potentials of low-quality modalities are not well explored in existing RGBT tracking algorithms. In this work, we propose a novel duality-gated mutual condition network to fully exploit the discriminative information of all modalities while suppressing the effects of data noise. In specific, we design a mutual condition module, which takes the discriminative information of a modality as the condition to guide feature learning of target appearance in another modality. Such module can effectively enhance target representations of all modalities even in the presence of low-quality modalities. To improve the quality of conditions and further reduce data noise, we propose a duality-gated mechanism and integrate it into the mutual condition module. To deal with the tracking failure caused by sudden camera motion, which often occurs in RGBT tracking, we design a resampling strategy based on optical flow algorithms. It does not increase much computational cost since we perform optical flow calculation only when the model prediction is unreliable and then execute resampling when the sudden camera motion is detected. Extensive experiments on four RGBT tracking benchmark datasets show that our method performs favorably against the state-of-the-art tracking algorithms