# Hyperspectral Mamba for Hyperspectral Object Tracking

arXiv: https://arxiv.org/abs/2509.08265 · HTML: https://mlsanity.com/paper/2509.08265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 66 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 30 of 31)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines](https://mlsanity.com/paper/1911.06188/) (2019)
- [SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking](https://mlsanity.com/paper/1911.07241/) (2019)
- [SwinTrack: A Simple and Strong Baseline for Transformer Tracking](https://mlsanity.com/paper/2112.00995/) (2021)
- [Deformable Siamese Attention Networks for Visual Object Tracking](https://mlsanity.com/paper/2004.06711/) (2020)
- [Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers](https://mlsanity.com/paper/2403.10574/) (2024)
- [HIPTrack: Visual Tracking with Historical Prompts](https://mlsanity.com/paper/2311.02072/) (2023)
- [ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe](https://mlsanity.com/paper/2312.17133/) (2023)
- [SUTrack: Towards Simple and Unified Single Object Tracking](https://mlsanity.com/paper/2412.19138/) (2024)
- [Exploring Enhanced Contextual Information for Video-Level Object Tracking](https://mlsanity.com/paper/2412.11023/) (2024)
- [Material Based Object Tracking in Hyperspectral Videos: Benchmark and Algorithms](https://mlsanity.com/paper/1812.04179/) (2018)
- [Separable Self and Mixed Attention Transformers for Efficient Object Tracking](https://mlsanity.com/paper/2309.03979/) (2023)
- [Towards Real-World Visual Tracking with Temporal Contexts](https://mlsanity.com/paper/2308.10330/) (2023)
- [SMamba: Sparse Mamba for Event-based Object Detection](https://mlsanity.com/paper/2501.11971/) (2025)
- [RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba](https://mlsanity.com/paper/2408.08827/) (2024)
- [High-Performance Transformer Tracking](https://mlsanity.com/paper/2203.13533/) (2022)
- [MambaEVT: Event Stream based Visual Object Tracking using State Space Model](https://mlsanity.com/paper/2408.10487/) (2024)
- [Hy-Tracker: A Novel Framework for Enhancing Efficiency and Accuracy of Object Tracking in Hyperspectral Videos](https://mlsanity.com/paper/2311.18199/) (2023)

## Built on by (most cited first, 2 of 2)

- [Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating](https://mlsanity.com/paper/2606.09167/) (2026)
- [MSP-Net: Manifold-Guided Spectral Prompt Network for Hyperspectral Object Tracking](https://mlsanity.com/paper/2608.09575/) (2026)

## Abstract (arXiv)

Hyperspectral object tracking holds great promise due to the rich spectral information and fine-grained material distinctions in hyperspectral images, which are beneficial in challenging scenarios. While existing hyperspectral trackers have made progress by either transforming hyperspectral data into false-color images or incorporating modality fusion strategies, they often fail to capture the intrinsic spectral information, temporal dependencies, and cross-depth interactions. To address these limitations, a new hyperspectral object tracking network equipped with Mamba (HyMamba), is proposed. It unifies spectral, cross-depth, and temporal modeling through state space modules (SSMs). The core of HyMamba lies in the Spectral State Integration (SSI) module, which enables progressive refinement and propagation of spectral features with cross-depth and temporal spectral information. Embedded within each SSI, the Hyperspectral Mamba (HSM) module is introduced to learn spatial and spectral information synchronously via three directional scanning SSMs. Based on SSI and HSM, HyMamba constructs joint features from false-color and hyperspectral inputs, and enhances them through interaction with original spectral features extracted from raw hyperspectral images. Extensive experiments conducted on seven benchmark datasets demonstrate that HyMamba achieves state-of-the-art performance. For instance, it achieves 73.0% of the AUC score and 96.3% of the DP@20 score on the HOTC2020 dataset. The code will be released at https://github.com/lgao001/HyMamba.