mlsanityData to 7 Oct 2026

Event-Adaptive State Transition and Gated Fusion for RGB-Event Object Tracking

arXiv 2604.13426 ↗First posted 15 Apr 2026cs.CV cs.AIcited by 0

On the map: State-space sequence models (S4, Mamba) → Point clouds, detection and remote sensing

Abstract (arXiv)

Existing Vision Mamba-based RGB-Event(RGBE) tracking methods suffer from using static state transition matrices, which fail to adapt to variations in event sparsity. This rigidity leads to imbalanced modeling-underfitting sparse event streams and overfitting dense ones-thus degrading cross-modal fusion robustness. To address these limitations, we propose MambaTrack, a multimodal and efficient tracking framework built upon a Dynamic State Space Model(DSSM). Our contributions are twofold. First, we introduce an event-adaptive state transition mechanism that dynamically modulates the state transition matrix based on event stream density. A learnable scalar governs the state evolution rate, enabling differentiated modeling of sparse and dense event flows. Second, we develop a Gated Projection Fusion(GPF) module for robust cross-modal integration. This module projects RGB features into the event feature space and generates adaptive gates from event density and RGB confidence scores. These gates precisely control the fusion intensity, suppressing noise while preserving complementary information. Experiments show that MambaTrack achieves state-of-the-art performance on the FE108 and FELT datasets. Its lightweight design suggests potential for real-time embedded deployment.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 36 of its 47 references that are in the map. First posted on arXiv 15 Apr 2026.

Builds on

36 of 47 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Mamba-FETrack: Frame-Event Tracking via State Space Model28 Apr
  2. 2024Fusion-Mamba for Cross-modality Object Detection14 Apr

Other references · 34

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  3. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  4. 2015Fast R-CNNcited by 2,930
  5. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  6. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
  7. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
  8. 2021Transformers in Vision: A Surveycited by 1,009
  9. 2024VMamba: Visual State Space Modelcited by 955
  10. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 935
  11. 2022Simplified State Space Layers for Sequence Modelingcited by 776
  12. 2021Transformer Trackingcited by 412
22 more
  1. 2019Learning Discriminative Model Prediction for Trackingcited by 344
  2. 2022Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Frameworkcited by 328
  3. 2018Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotioncited by 304
  4. 2018ATOM: Accurate Tracking by Overlap Maximizationcited by 285
  5. 2019End-to-End Learning of Representations for Asynchronous Event-Based Datacited by 216
  6. 2018Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Carscited by 197
  7. 2020Siamese Box Adaptive Network for Visual Trackingcited by 189
  8. 2019SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelinescited by 177
  9. 2022Recurrent Vision Transformers for Object Detection with Event Camerascited by 158
  10. 2024EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mambacited by 146
  11. 2023Visual Prompt Multi-Modal Trackingcited by 141
  12. 2021VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flowscited by 128
  13. 2020Know Your Surroundings: Exploiting Scene Information for Object Trackingcited by 119
  14. 2021Object Tracking by Jointly Exploiting Frame and Event Domaincited by 83
  15. 2023Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baselinecited by 73
  16. 2024Pan-Mamba: Effective pan-sharpening with State Space Modelcited by 73
  17. 2018Continuous-time Intensity Estimation Using Event Camerascited by 67
  18. 2019Deep Learning for Visual Tracking: A Comprehensive Surveycited by 60
  19. 2022Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metriccited by 51
  20. 2023Frame-Event Alignment and Fusion Network for High Frame Rate Trackingcited by 43
  21. 2023Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackerscited by 43
  22. 2023Event-Enhanced Multi-Modal Spiking Neural Network for Dynamic Obstacle Avoidancecited by 8

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  1. 2025Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking
  2. 2026FreqTrack: Frequency Learning based Vision Transformer for RGB-Event Object Tracking
  3. 2024MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking
  4. 2026UBATrack: Spatio-Temporal State Space Model for General Multi-Modal Tracking
  5. 2024Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking
  6. 2026Dynamic Pondering Sparsity-aware Mixture-of-Experts Transformer for Event Stream based Visual Object Tracking
  7. 2025Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
  8. 2024RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba