Robotics & Embodied AI
A 0.32-million-parameter adapter outperforms full fine-tuning and prompt learning in multimodal object tracking
In a 2024 paper in Proceedings of the AAAI Conference on Artificial Intelligence, a peer-reviewed conference, researchers at Tianjin University report a small adapter for object tracking. The adapter adds only 0.32 million trainable parameters, the adjustable values a model learns during training. In the authors' comparison, this lightweight method delivered better tracking performance than full fine-tuning methods, which update all of the model's parameters, and prompt-learning methods, which train only a small set of learned signals.
What problem does the work address?
Object trackers often rely on a single RGB camera, which does not always work well in changing weather or light. Multi-modal inputs, such as RGB and infrared, are one way to compensate for that weakness in all-weather tracking, the paper explains. But gathering enough multi-modal tracking data is hard, and the most useful input type can change with the surroundings. Existing methods often fail to pull complementary information from different modalities as conditions change, the authors write.
How does the new model combine modalities?
The model uses a universal bi-directional adapter, a small part that lets different imaging modalities exchange information. Several modality-specific transformer encoder branches, a kind of neural network module, share parameters. Each branch processes one input type using a frozen pre-trained foundation model, which stays unchanged during training. A light feature adapter then passes modality-specific information from one modality to another and fuses visual features adaptively.
Why does the small parameter count matter?
The adapter adds 0.32 million trainable parameters. The authors report that this approach still achieved better tracking performance than both full fine-tuning methods and prompt-learning methods. That means the model stays comparatively lightweight while improving tracking, according to the paper.
What should a reader know about the setup?
What does multimodal object tracking mean here?
In this paper, multimodal means inputs from more than one kind of imaging sensor, such as RGB and infrared. The goal is to track an object in changing environments.
Did the authors make the code available?
The authors state that they have made the code available.
Where did this work appear?
The authors published it in 2024 in Proceedings of the AAAI Conference on Artificial Intelligence, a peer-reviewed conference.
Limits and what's next
This is one conference paper. The abstract describes a model comparison on multimodal tracking, but it does not name the datasets used, so a reader cannot tell the exact test conditions from the summary. The abstract reports no independent replication or external validation. It also notes that collecting enough multimodal tracking data is hard and that the dominant modality can change with the environment, which is part of the challenge the design aims to address.
Source: Bing Cao et al., “Bi-directional Adapter for Multimodal Tracking,” Proceedings of the AAAI Conference on Artificial Intelligence, 2024.