UBATrack: Spatio-Temporal State Space Model for General Multi-Modal Tracking

• URL: http://arxiv.org/abs/2601.14799v1
• Date: Wed, 21 Jan 2026 09:24:19 GMT
• Title: UBATrack: Spatio-Temporal State Space Model for General Multi-Modal Tracking
• Authors: Qihua Liang, Liang Chen, Yaozong Zheng, Jian Nong, Zhiyi Mo, Bineng Zhong,
• Abstract summary: We introduce a novel multi-modal tracking framework based on a mamba-style state, termed UBATrack.<n>UBATrack comprises two simple yet effective work space: a S-temporal Mamba Adapter (MA) and a Dynamic Multi-modal Feature Mixer.<n>Experiments show that UBATrack outperforms state-of-the-art methods on RGB-T, RGB-D, and RGB-E tracking benchmarks.
• Score: 40.8191099453086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-modal object tracking has attracted considerable attention by integrating multiple complementary inputs (e.g., thermal, depth, and event data) to achieve outstanding performance. Although current general-purpose multi-modal trackers primarily unify various modal tracking tasks (i.e., RGB-Thermal infrared, RGB-Depth or RGB-Event tracking) through prompt learning, they still overlook the effective capture of spatio-temporal cues. In this work, we introduce a novel multi-modal tracking framework based on a mamba-style state space model, termed UBATrack. Our UBATrack comprises two simple yet effective modules: a Spatio-temporal Mamba Adapter (STMA) and a Dynamic Multi-modal Feature Mixer. The former leverages Mamba's long-sequence modeling capability to jointly model cross-modal dependencies and spatio-temporal visual cues in an adapter-tuning manner. The latter further enhances multi-modal representation capacity across multiple feature dimensions to improve tracking robustness. In this way, UBATrack eliminates the need for costly full-parameter fine-tuning, thereby improving the training efficiency of multi-modal tracking algorithms. Experiments show that UBATrack outperforms state-of-the-art methods on RGB-T, RGB-D, and RGB-E tracking benchmarks, achieving outstanding results on the LasHeR, RGBT234, RGBT210, DepthTrack, VOT-RGBD22, and VisEvent datasets.

Related papers

• UETrack: A Unified and Efficient Framework for Single Object Tracking [46.50641228786134]
  UETrack is an efficient framework for single object tracking.<n>It efficiently handles multiple modalities including RGB, Depth, Thermal, Event, and Language.<n>It achieves a superior speed-accuracy trade-off compared to previous methods.
  arXiv  Detail & Related papers  (2026-03-02T03:32:30Z)
• Learning Frequency and Memory-Aware Prompts for Multi-Modal Object Tracking [74.15663758681849]
  We present Learning Frequency and Memory-Aware Prompts, a dual-adapter framework that injects lightweight prompts into a frozen RGB tracker.<n>A frequency-guided visual adapter adaptively transfers complementary cues across modalities.<n>A multilevel memory adapter with short, long, and permanent memory stores, updates, and retrieves reliable temporal context.
  arXiv  Detail & Related papers  (2025-06-30T15:38:26Z)
• Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking [9.353589376846902]
  We propose an efficient RGB-Event object tracking framework based on the linear-complexity Vision Mamba network.<n>The source code and pre-trained models will be released at https://github.com/Event-AHU/Mamba_FETrack.
  arXiv  Detail & Related papers  (2025-06-30T12:24:01Z)
• Diff-MM: Exploring Pre-trained Text-to-Image Generation Model for Unified Multi-modal Object Tracking [45.341224888996514]
  Multi-modal object tracking integrates auxiliary modalities such as depth, thermal infrared, event flow, and language.<n>Existing methods typically start from an RGB-based tracker and learn to understand auxiliary modalities only from training data.<n>This work proposes a unified multi-modal tracker Diff-MM by exploiting the multi-modal understanding capability of the pre-trained text-to-image generation model.
  arXiv  Detail & Related papers  (2025-05-19T01:42:13Z)
• CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object Tracking [68.24998698508344]
  We introduce CAMEL, a novel association module for Context-Aware Multi-Cue ExpLoitation.<n>Unlike end-to-end detection-by-tracking approaches, our method remains lightweight and fast to train while being able to leverage external off-the-shelf models.<n>Our proposed online tracking pipeline, CAMELTrack, achieves state-of-the-art performance on multiple tracking benchmarks.
  arXiv  Detail & Related papers  (2025-05-02T13:26:23Z)
• SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking [19.50096632818305]
  Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Recent studies have utilized prompt tuning to transfer pre-trained RGB-based trackers to multimodal data. We propose a novel symmetric multimodal tracking framework called SDSTrack.
  arXiv  Detail & Related papers  (2024-03-24T04:15:50Z)
• Bi-directional Adapter for Multi-modal Tracking [67.01179868400229]
  We propose a novel multi-modal visual prompt tracking model based on a universal bi-directional adapter. We develop a simple but effective light feature adapter to transfer modality-specific information from one modality to another. Our model achieves superior tracking performance in comparison with both the full fine-tuning methods and the prompt learning-based methods.
  arXiv  Detail & Related papers  (2023-12-17T05:27:31Z)
• Single-Model and Any-Modality for Video Object Tracking [85.83753760853142]
  We introduce Un-Track, a Unified Tracker of a single set of parameters for any modality. To handle any modality, our method learns their common latent space through low-rank factorization and reconstruction techniques. Our Un-Track achieves +8.1 absolute F-score gain, on the DepthTrack dataset, by introducing only +2.14 (over 21.50) GFLOPs with +6.6M (over 93M) parameters.
  arXiv  Detail & Related papers  (2023-11-27T14:17:41Z)
• Visual Prompt Multi-Modal Tracking [71.53972967568251]
  Visual Prompt multi-modal Tracking (ViPT) learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to various downstream multimodal tracking tasks. ViPT outperforms the full fine-tuning paradigm on multiple downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event tracking.
  arXiv  Detail & Related papers  (2023-03-20T01:51:07Z)
• Prompting for Multi-Modal Tracking [70.0522146292258]
  We propose a novel multi-modal prompt tracker (ProTrack) for multi-modal tracking. ProTrack can transfer the multi-modal inputs to a single modality by the prompt paradigm. Our ProTrack can achieve high-performance multi-modal tracking by only altering the inputs, even without any extra training on multi-modal data.
  arXiv  Detail & Related papers  (2022-07-29T09:35:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.