Cross-Modal Object Tracking via Modality-Aware Fusion Network and A Large-Scale Dataset
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Abstract (arXiv)
Visual tracking often faces challenges such as invalid targets and decreased performance in low-light conditions when relying solely on RGB image sequences. While incorporating additional modalities like depth and infrared data has proven effective, existing multi-modal imaging platforms are complex and lack real-world applicability. In contrast, near-infrared (NIR) imaging, commonly used in surveillance cameras, can switch between RGB and NIR based on light intensity. However, tracking objects across these heterogeneous modalities poses significant challenges, particularly due to the absence of modality switch signals during tracking. To address these challenges, we propose an adaptive cross-modal object tracking algorithm called Modality-Aware Fusion Network (MAFNet). MAFNet efficiently integrates information from both RGB and NIR modalities using an adaptive weighting mechanism, effectively bridging the appearance gap and enabling a modality-aware target representation. It consists of two key components: an adaptive weighting module and a modality-specific representation module......
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 43 of its 73 references that are in the map. First posted on arXiv 22 Dec 2023.
Builds on
43 of 73 references.
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31 more
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Built on by
Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2025VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusioncited by 20
- 2025Omni Survey for Multimodality Analysis in Visual Object Trackingcited by 2
- 2025OmniScene: Attention-Augmented Multimodal 4D Scene Understanding for Autonomous Drivingcited by 2
- 2025SwiTrack: Tri-State Switch for Cross-Modal Object Trackingcited by 1
- 2026UAV traffic scene understanding: A regulation embedded multi-modal network and a unified benchmarkcited by 1
- 2026Unified Multimodal Visual Tracking with Dual Mixture-of-Expertscited by 1
- 2026Cross-Modal UAV Object Tracking: State-Aware Representation Learning and A Unified Benchmarkcited by 0
- 2026Template-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Trackingcited by 0
- 2026Template-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Tracking29 Sep 2026
- 2026Cross-Modal UAV Object Tracking: State-Aware Representation Learning and A Unified Benchmark21 Jul 2026
- 2026Unified Multimodal Visual Tracking with Dual Mixture-of-Experts5 May 2026
- 2026UAV traffic scene understanding: A regulation embedded multi-modal network and a unified benchmark11 Mar 2026
- 2025SwiTrack: Tri-State Switch for Cross-Modal Object Tracking20 Nov 2025
- 2025OmniScene: Attention-Augmented Multimodal 4D Scene Understanding for Autonomous Driving24 Sep 2025
- 2025Omni Survey for Multimodality Analysis in Visual Object Tracking18 Aug 2025
- 2025VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion25 Feb 2025