# Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detection

arXiv: https://arxiv.org/abs/2406.06949 · HTML: https://mlsanity.com/paper/2406.06949/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 61 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [TDN: Temporal Difference Networks for Efficient Action Recognition](https://mlsanity.com/paper/2012.10071/) (2020)
- [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)
- [Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2005.03572/) (2020)
- [Infrared Small Target Detection with Scale and Location Sensitivity](https://mlsanity.com/paper/2403.19366/) (2024)
- [Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark](https://mlsanity.com/paper/2206.13996/) (2022)
- [Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2405.04964/) (2024)
- [RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection](https://mlsanity.com/paper/2311.00917/) (2023)
- [Local-Global Temporal Difference Learning for Satellite Video Super-Resolution](https://mlsanity.com/paper/2304.04421/) (2023)
- [SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection](https://mlsanity.com/paper/2403.05416/) (2024)
- [EFLNet: Enhancing Feature Learning for Infrared Small Target Detection](https://mlsanity.com/paper/2307.14723/) (2023)

## Built on by (most cited first, 15 of 15)

- [BeltCrack: the First Sequential-image Industrial Conveyor Belt Crack Detection Dataset and Its Baseline with Triple-domain Feature Learning](https://mlsanity.com/paper/2506.17892/) (2025)
- [TY-RIST: Tactical YOLO Tricks for Real-time Infrared Small Target Detection](https://mlsanity.com/paper/2509.22909/) (2025)
- [One Shot is Enough for Sequential Infrared Small Target Segmentation](https://mlsanity.com/paper/2408.04823/) (2024)
- [Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2507.02454/) (2025)
- [Point-to-Mask: From Arbitrary Point Annotations to Mask-Level Infrared Small Target Detection](https://mlsanity.com/paper/2603.16257/) (2026)
- [Dim and Small Target Detection for Drone Broadcast Frames Based on Time-Frequency Analysis](https://mlsanity.com/paper/2505.18167/) (2025)
- [HyperTea: A Hypergraph-based Temporal Enhancement and Alignment Network for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2508.10678/) (2025)
- [You Only Look Omni Gradient Backpropagation for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2511.13013/) (2025)
- [STGBD-Net: Spatio-temporal Gradient Basis Decomposition Network for Infrared Small Target Detection](https://mlsanity.com/paper/2512.03470/) (2025)
- [SPIRIT: Adapting Vision Foundation Models for Unified Single- and Multi-Frame Infrared Small Target Detection](https://mlsanity.com/paper/2602.01843/) (2026)
- [TriC-Motion: Tri-Domain Causal Modeling Grounded Text-to-Motion Generation](https://mlsanity.com/paper/2602.08462/) (2026)
- [MI-DETR: A Strong Baseline for Moving Infrared Small Target Detection with Motion Integration](https://mlsanity.com/paper/2603.05071/) (2026)
- [Decoupled Motion Representation Learning for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2606.15286/) (2026)
- [Gaze-DETR: Top-Down Guidance Through Priority Maps for Infrared Weak-Small UAV Detection with DETR](https://mlsanity.com/paper/2607.19040/) (2026)
- [RDANet: Relative Degradation Aware Network for Infrared Small Target Detection](https://mlsanity.com/paper/2608.20870/) (2026)

## Abstract (arXiv)

As a sub-field of object detection, moving infrared small target detection presents significant challenges due to tiny target sizes and low contrast against backgrounds. Currently-existing methods primarily rely on the features extracted only from spatio-temporal domain. Frequency domain has hardly been concerned yet, although it has been widely applied in image processing. To extend feature source domains and enhance feature representation, we propose a new Triple-domain Strategy (Tridos) with the frequency-aware memory enhancement on spatio-temporal domain for infrared small target detection. In this scheme, it effectively detaches and enhances frequency features by a local-global frequency-aware module with Fourier transform. Inspired by human visual system, our memory enhancement is designed to capture the spatial relations of infrared targets among video frames. Furthermore, it encodes temporal dynamics motion features via differential learning and residual enhancing. Additionally, we further design a residual compensation to reconcile possible cross-domain feature mismatches. To our best knowledge, proposed Tridos is the first work to explore infrared target feature learning comprehensively in spatio-temporal-frequency domains. The extensive experiments on three datasets (i.e., DAUB, ITSDT-15K and IRDST) validate that our triple-domain infrared feature learning scheme could often be obviously superior to state-of-the-art ones. Source codes are available at https://github.com/UESTC-nnLab/Tridos.