# DATransNet: Dynamic Attention Transformer Network for Infrared Small Target Detection

arXiv: https://arxiv.org/abs/2409.19599 · HTML: https://mlsanity.com/paper/2409.19599/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Asymmetric Contextual Modulation for Infrared Small Target Detection](https://mlsanity.com/paper/2009.14530/) (2020)
- [Dense Nested Attention Network for Infrared Small Target Detection](https://mlsanity.com/paper/2106.00487/) (2021)
- [UIU-Net: U-Net in U-Net for Infrared Small Object Detection](https://mlsanity.com/paper/2212.00968/) (2022)
- [Attentional Local Contrast Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2012.08573/) (2020)
- [Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection](https://mlsanity.com/paper/1703.09157/) (2017)
- [SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection](https://mlsanity.com/paper/2401.15583/) (2024)
- [IRSAM: Advancing Segment Anything Model for Infrared Small Target Detection](https://mlsanity.com/paper/2407.07520/) (2024)
- [Unleashing the Power of Generic Segmentation Models: A Simple Baseline for Infrared Small Target Detection](https://mlsanity.com/paper/2409.04714/) (2024)

## Built on by (most cited first, 12 of 12)

- [CSPENet: Contour-Aware and Saliency Priors Embedding Network for Infrared Small Target Detection](https://mlsanity.com/paper/2505.09943/) (2025)
- [LAM-YOLO: Drones-based Small Object Detection on Lighting-Occlusion Attention Mechanism YOLO](https://mlsanity.com/paper/2411.00485/) (2024)
- [WIFE-Fusion:Wavelet-aware Intra-inter Frequency Enhancement for Multi-model Image Fusion](https://mlsanity.com/paper/2506.03555/) (2025)
- [FSATFusion: Frequency-Spatial Attention Transformer for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2506.10366/) (2025)
- [DSCSNet: A Dynamic Sparse Compression Sensing Network for Closely-Spaced Infrared Small Target Unmixing](https://mlsanity.com/paper/2603.21192/) (2026)
- [SWAN: Synergistic Wavelet-Attention Network for Infrared Small Target Detection](https://mlsanity.com/paper/2508.01322/) (2025)
- [Dual-Domain Perspective on Degradation-Aware Fusion: A VLM-Guided Robust Infrared and Visible Image Fusion Framework](https://mlsanity.com/paper/2509.05000/) (2025)
- [Anomaly-Aware YOLO: A Frugal yet Robust Approach to Infrared Small Target Detection](https://mlsanity.com/paper/2510.04741/) (2025)
- [STGBD-Net: Spatio-temporal Gradient Basis Decomposition Network for Infrared Small Target Detection](https://mlsanity.com/paper/2512.03470/) (2025)
- [FeedbackSTS-Det: Sparse Frames-Based Spatio-Temporal Semantic Feedback Network for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2601.14690/) (2026)
- [Rethinking Representations for Cross-Domain Infrared Small Target Detection: A Generalizable Perspective from the Frequency Domain](https://mlsanity.com/paper/2604.01934/) (2026)
- [LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection](https://mlsanity.com/paper/2607.24184/) (2026)

## Abstract (arXiv)

Infrared small target detection (ISTD) is widely used in civilian and military applications. However, ISTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds. To address this issue, we propose the Dynamic Attention Transformer Network (DATransNet), which aims to extract and preserve detailed information vital for small targets. DATransNet employs the Dynamic Attention Transformer (DATrans), simulating central difference convolutions (CDC) to extract gradient features. Furthermore, we propose a global feature extraction module (GFEM) that offers a comprehensive perspective to prevent the network from focusing solely on details while neglecting the global information. We compare the network with state-of-the-art (SOTA) approaches and demonstrate that our method performs effectively. Our source code is available at https://github.com/greekinRoma/DATransNet.