ArXiv

CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection

2026-08-06 · 15:59 UTC ·Nima Hatami, Karim Faez, Saeed Sharifian... ·1 min read
Authors
Nima Hatami, Karim Faez, Saeed Sharifian...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2608.06205v1
PDF
https://arxiv.org/pdf/2608.06205v1

Brief

CFGPNet is an RGB–T multispectral object detection framework that addresses weak cross-modal interaction, modality distribution gaps, and heavy attention costs by combining a re-parameterized GELAN backbone (RepViT-style), a lightweight Cross Computation Efficient Attention (CrossCEA), an Attention Selection and Aggregation Fusion (ASAF) module, and programmable-gradient auxiliary branches. On five benchmarks (FLIR, M3FD, LLVIP, VEDAI, MFAD) it reports strong mAP50 and mAP50:95 gains; code is released.

Why it matters

CFGPNet obtains strong results on five multispectral benchmarks: FLIR 80.7% mAP50 / 45.0% mAP50:95; M3FD 89.9% / 63.4%; LLVIP 97.8% / 68.9%; VEDAI 83.3% / 56.9%; MFAD 83.4% / 61.8%.

Key details

  • Key components: improved GELAN backbone with RepViT-style re-parameterized blocks, Cross Computation Efficient Attention (CrossCEA) for efficient cross-modal interaction, Attention Selection and Aggregation Fusion (ASAF) for compact fused representations, and a programmable-gradient auxiliary branch to improve optimization.
  • ArXiv preprint (2026-08-06) reports practical accuracy–efficiency trade-offs across three model scales; code, data, and fine-tuned models are available at https://github.com/NimaHatami99/CFGPNet.
Source evidence

Abstract

RGB--T object detection exploits the complementary strengths of visible and infrared imagery, supporting robust perception in low-light, adverse-weather, and complex multi-scale environments. However, existing methods still suffer from insufficient cross-modal interaction, unstable fusion from modality distribution gaps, and the high computational cost of heavy attention-based architectures. To address these issues, CFGPNet is proposed, a Cross-Attention-Based Fused Gradient Programmed Network framework for multispectral object detection. CFGPNet uses an improved GELAN backbone with RepViT-style re-parameterized blocks to strengthen feature representation while preserving computational efficiency. A Cross Computation Efficient Attention (CrossCEA) module is introduced to enhance cross-modal feature interaction and reduce redundant information transfer between visible and thermal branches. To generate compact and discriminative fused representations, an Attention Selection and Aggregation Fusion (ASAF) network combines dense feature aggregation with selective attention-based emphasis. Moreover, a programmable-gradient auxiliary branch is integrated into each CFGPNet variant to improve gradient delivery and optimization quality. Experiments on five public multispectral benchmarks, FLIR, M3FD, LLVIP, VEDAI, and MFAD, demonstrate that CFGPNet achieves strong and consistent performance across diverse scenes, object scales, and modality balances. In particular, the framework attains 80.7% mAP50 / 45.0% mAP50:95 on FLIR, 89.9% / 63.4% on M3FD, and 97.8% / 68.9% on LLVIP. It also reaches 83.3% / 56.9% on VEDAI and 83.4% / 61.8% on MFAD. These results show that CFGPNet is an effective, practical solution offering useful accuracy--efficiency trade-offs across three model scales. The code, data, and fine-tuned models are available at https://github.com/NimaHatami99/CFGPNet.