RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba

TL;DR

This paper introduces AINet, a novel RGBT tracking network that performs efficient, all-layer multimodal interactions using progressive fusion, significantly improving robustness and performance in multimodal tracking tasks.

Contribution

The paper proposes a new All-layer multimodal Interaction Network with a Difference-based Fusion Mamba and Order-dynamic Fusion Mamba for efficient, comprehensive feature interaction across all layers.

Findings

Achieves state-of-the-art performance on four RGBT tracking datasets.

Effectively balances interaction capability and computational efficiency.

Demonstrates robustness and accuracy improvements over existing methods.

Abstract

Existing RGBT tracking methods often design various interaction models to perform cross-modal fusion of each layer, but can not execute the feature interactions among all layers, which plays a critical role in robust multimodal representation, due to large computational burden. To address this issue, this paper presents a novel All-layer multimodal Interaction Network, named AINet, which performs efficient and effective feature interactions of all modalities and layers in a progressive fusion Mamba, for robust RGBT tracking. Even though modality features in different layers are known to contain different cues, it is always challenging to build multimodal interactions in each layer due to struggling in balancing interaction capabilities and efficiency. Meanwhile, considering that the feature discrepancy between RGB and thermal modalities reflects their complementary information to some extent, we design a Difference-based Fusion Mamba (DFM) to achieve enhanced fusion of different modalities with linear complexity. When interacting with features from all layers, a huge number of token sequences (3840 tokens in this work) are involved and the computational burden is thus large. To handle this problem, we design an Order-dynamic Fusion Mamba (OFM) to execute efficient and effective feature interactions of all layers by dynamically adjusting the scan order of different layers in Mamba. Extensive experiments on four public RGBT tracking datasets show that AINet achieves leading performance against existing state-of-the-art methods.