# End-to-End Self-Supervised RGB-T Tracking without Modality Misleading

arXiv: https://arxiv.org/abs/2609.37162 · HTML: https://mlsanity.com/paper/2609.37162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Visual Prompt Multi-Modal Tracking](https://mlsanity.com/paper/2303.10826/) (2023)
- [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)
- [Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2204.04120/) (2022)
- [Unsupervised Deep Tracking](https://mlsanity.com/paper/1904.01828/) (2019)
- [Diff-Tracker: Text-to-Image Diffusion Models are Unsupervised Trackers](https://mlsanity.com/paper/2407.08394/) (2024)
- [Learning to Track Objects from Unlabeled Videos](https://mlsanity.com/paper/2108.12711/) (2021)
- [Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2304.14394/) (2023)
- [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)
- [MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking](https://mlsanity.com/paper/2408.07889/) (2024)
- [Self-supervised Object Tracking with Cycle-consistent Siamese Networks](https://mlsanity.com/paper/2008.00637/) (2020)
- [CADTrack: Learning Contextual Aggregation with Deformable Alignment for Robust RGBT Tracking](https://mlsanity.com/paper/2511.17967/) (2025)
- [Large Margin Object Tracking with Circulant Feature Maps](https://mlsanity.com/paper/1703.05020/) (2017)
- [AFter: Attention-based Fusion Router for RGBT Tracking](https://mlsanity.com/paper/2405.02717/) (2024)
- [Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking](https://mlsanity.com/paper/2505.03507/) (2025)

## Abstract (arXiv)

RGB-T object tracking leverages the complementary characteristics of visible and thermal infrared modalities to improve robustness under adverse conditions. Existing supervised methods typically rely on costly modality-aligned bounding box annotations, while most self-supervised approaches follow a two-stage pseudo-labeling paradigm, making tracker training sensitive to pseudo-label quality and preventing joint end-to-end optimization. In this paper, we propose ESMTrack, a fully end-to-end self-supervised RGB-T tracking framework without offline pseudo-label generation or dense frame-level bounding box annotations. Given only the standard initial-frame annotation used in visual tracking, ESMTrack learns discriminative and temporally consistent representations through two complementary objectives: a grounding triplet loss on annotated initial frames and a cross-frame temporal triplet loss on unlabeled search frames, with reliable samples selected by forward-backward consistency. To address modality dominance bias, ESMTrack employs a three-branch architecture consisting of a fusion branch and two unimodal branches for RGB and thermal inputs. We quantify modality contributions using the Average Peak-to-Correlation Energy by measuring response discrepancies between the fusion and unimodal branches. The resulting reliability estimates guide a training-time modality decoupling mechanism that suppresses dominant-modality shortcuts and adaptively weights cross-modal contrastive learning for task-level alignment. Extensive experiments on five RGB-T tracking benchmarks show that ESMTrack achieves competitive state-of-the-art performance, strong cross-dataset generalization, and real-time inference speed. The source code is available at https://github.com/LiShenglana/ESMTrack.