DMTrack: Spatio-Temporal Multimodal Tracking via Dual-Adapter
- URL: http://arxiv.org/abs/2508.01592v1
- Date: Sun, 03 Aug 2025 05:13:27 GMT
- Title: DMTrack: Spatio-Temporal Multimodal Tracking via Dual-Adapter
- Authors: Weihong Li, Shaohua Dong, Haonan Lu, Yanhao Zhang, Heng Fan, Libo Zhang,
- Abstract summary: We introduce a novel dual-temporal architecture for multimodal tracking, dubbed DMTrack.<n>Designs achieve promising- multimodal tracking performance with merely bfbf0.93M trainable parameters.<n>Experiments on five benchmarks show that DMTrack achieves state-of-the-art results.
- Score: 27.594612913364447
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we explore adapter tuning and introduce a novel dual-adapter architecture for spatio-temporal multimodal tracking, dubbed DMTrack. The key of our DMTrack lies in two simple yet effective modules, including a spatio-temporal modality adapter (STMA) and a progressive modality complementary adapter (PMCA) module. The former, applied to each modality alone, aims to adjust spatio-temporal features extracted from a frozen backbone by self-prompting, which to some extent can bridge the gap between different modalities and thus allows better cross-modality fusion. The latter seeks to facilitate cross-modality prompting progressively with two specially designed pixel-wise shallow and deep adapters. The shallow adapter employs shared parameters between the two modalities, aiming to bridge the information flow between the two modality branches, thereby laying the foundation for following modality fusion, while the deep adapter modulates the preliminarily fused information flow with pixel-wise inner-modal attention and further generates modality-aware prompts through pixel-wise inter-modal attention. With such designs, DMTrack achieves promising spatio-temporal multimodal tracking performance with merely \textbf{0.93M} trainable parameters. Extensive experiments on five benchmarks show that DMTrack achieves state-of-the-art results. Code will be available.
Related papers
- FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation [50.438552588818]
We propose textbfFindRec (textbfFlexible unified textbfinformation textbfdisentanglement for multi-modal sequential textbfRecommendation)<n>A Stein kernel-based Integrated Information Coordination Module (IICM) theoretically guarantees distribution consistency between multimodal features and ID streams.<n>A cross-modal expert routing mechanism that adaptively filters and combines multimodal features based on their contextual relevance.
arXiv Detail & Related papers (2025-07-07T04:09:45Z) - Visual and Memory Dual Adapter for Multi-Modal Object Tracking [34.406308400305385]
We propose a novel visual and memory dual adapter (VMDA) to construct more robust representations for multi-modal tracking.<n>We develop a simple but effective visual adapter that adaptively transfers discriminative cues from auxiliary modality to dominant modality.<n>We also design the memory adapter inspired by the human memory mechanism, which stores global temporal cues and performs dynamic update and retrieval operations.
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) - 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) - DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing [8.530409994516619]
Multispectral oriented object detection faces challenges due to both inter-modal and intra-modal discrepancies.
We propose Disparity-guided Multispectral Mamba (DMM), a framework comprised of a Disparity-guided Cross-modal Fusion Mamba (DCFM) module, a Multi-scale Target-aware Attention (MTA) module, and a Target-Prior Aware (TPA) auxiliary task.
arXiv Detail & Related papers (2024-07-11T02:09:59Z) - 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) - AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation [80.33846577924363]
We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video framegithub.
It is based on two essential designs. First, we build bidirectional volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations.
Second, we derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately.
arXiv Detail & Related papers (2023-04-19T16:18:47Z) - PSNet: Parallel Symmetric Network for Video Salient Object Detection [85.94443548452729]
We propose a VSOD network with up and down parallel symmetry, named PSNet.
Two parallel branches with different dominant modalities are set to achieve complete video saliency decoding.
arXiv Detail & Related papers (2022-10-12T04:11:48Z) - 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.