# DDAF-Net: Decoupled and Differentiated Attention Fusion Network for Object Detection

## TL;DR

DDAF-Net is a new network for object detection that improves performance by combining RGB and infrared data more effectively.

## Contribution

The paper introduces a decoupled and differentiated attention fusion framework for RGB–IR object detection.

## Key findings

- DDAF-Net achieves state-of-the-art performance on the LLVIP and M3FD datasets.
- The decoupling–enhancement–fusion paradigm effectively addresses modality redundancy and noise interference.
- Differentiated attention mechanisms improve cross-modal alignment and suppress noise.

## Abstract

What are the main findings?
A decoupled RGB–IR framework explicitly separates modality-common and -specific features, minimizing redundancy while retaining complementary details.A differentiated attention strategy robustly aligns cross-modal semantics and suppresses noise, achieving state-of-the-art performance on LLVIP and M3FD datasets.

A decoupled RGB–IR framework explicitly separates modality-common and -specific features, minimizing redundancy while retaining complementary details.

A differentiated attention strategy robustly aligns cross-modal semantics and suppresses noise, achieving state-of-the-art performance on LLVIP and M3FD datasets.

What are the implications of the main findings?
The proposed “decoupling–enhancement–fusion” paradigm offers a robust solution to modality redundancy, spatial misalignment, and noise interference.Its lightweight attention and adaptive gating mechanisms provide an efficient design extensible to other heterogeneous sensor fusion tasks.

The proposed “decoupling–enhancement–fusion” paradigm offers a robust solution to modality redundancy, spatial misalignment, and noise interference.

Its lightweight attention and adaptive gating mechanisms provide an efficient design extensible to other heterogeneous sensor fusion tasks.

The fusion of data from visible (RGB) and infrared (IR) sensors is essential for robust all-day and all-weather object detection. However, existing methods often suffer from modality redundancy and noise interference. To address these challenges, we propose the Decoupled and Differentiated Attention Fusion Network (DDAF-Net). Architecturally, DDAF-Net employs a decoupled backbone with a Siamese weight-sharing strategy to extract modality-common features, while parallel branches capture modality-specific features. To effectively integrate these features, we design the Differentiated Attention Fusion Module (DAFM). First, we introduce Spatial Residual Unshuffle Embedding (SRUE) to achieve lossless downsampling while preserving global semantic information. Second, differentiated attention mechanisms are applied for feature enhancement: Dual-Norm Alignment Attention (DNAA) facilitates effective modal alignment and enhances semantic consistency in modality-common features, while Sparse Purification Attention (SPA) enables selective utilization of complementary information by suppressing noise and focusing on salient regions in modality-specific features. Finally, the Adaptive Complementary Fusion Module (ACFM) integrates these components by using modality-common features as a baseline and dynamically weighting the complementary modality-specific information. Extensive experiments on public datasets such as LLVIP and M3FD demonstrate that DDAF-Net achieves state-of-the-art performance. These results validate the effectiveness of our proposed decoupling–enhancement–fusion paradigm.