# SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection

arXiv: https://arxiv.org/abs/2403.05416 · HTML: https://mlsanity.com/paper/2403.05416/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 64 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization](https://mlsanity.com/paper/2006.01791/) (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)
- [Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity](https://mlsanity.com/paper/2102.03065/) (2021)
- [UIU-Net: U-Net in U-Net for Infrared Small Object Detection](https://mlsanity.com/paper/2212.00968/) (2022)
- [More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification](https://mlsanity.com/paper/2008.05457/) (2020)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [Attentional Local Contrast Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2012.08573/) (2020)
- [Self-Supervised GANs via Auxiliary Rotation Loss](https://mlsanity.com/paper/1811.11212/) (2018)
- [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)
- [EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2310.19288/) (2023)
- [AlignMixup: Improving Representations By Interpolating Aligned Features](https://mlsanity.com/paper/2103.15375/) (2021)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers](https://mlsanity.com/paper/2210.07562/) (2022)
- [Dense Dilated Convolutions Merging Network for Land Cover Classification](https://mlsanity.com/paper/2003.04027/) (2020)
- [Magic ELF: Image Deraining Meets Association Learning and Transformer](https://mlsanity.com/paper/2207.10455/) (2022)

## Built on by (most cited first, 17 of 17)

- [Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2406.06949/) (2024)
- [Deep learning based infrared small object segmentation: Challenges and future directions](https://mlsanity.com/paper/2502.14168/) (2025)
- [Rethinking Generalizable Infrared Small Target Detection: A Real-scene Benchmark and Cross-view Representation Learning](https://mlsanity.com/paper/2504.16487/) (2025)
- [Scaling Up Single Image Dehazing Algorithm by Cross-Data Vision Alignment for Richer Representation Learning and Beyond](https://mlsanity.com/paper/2407.14823/) (2024)
- [DFVO: Learning Darkness-free Visible and Infrared Image Disentanglement and Fusion All at Once](https://mlsanity.com/paper/2505.04526/) (2025)
- [TY-RIST: Tactical YOLO Tricks for Real-time Infrared Small Target Detection](https://mlsanity.com/paper/2509.22909/) (2025)
- [Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2507.02454/) (2025)
- [Ivan-ISTD: Rethinking Cross-domain Heteroscedastic Noise Perturbations in Infrared Small Target Detection](https://mlsanity.com/paper/2510.12241/) (2025)
- [Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark](https://mlsanity.com/paper/2604.02773/) (2026)
- [Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection](https://mlsanity.com/paper/2407.07289/) (2024)
- [HyperTea: A Hypergraph-based Temporal Enhancement and Alignment Network for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2508.10678/) (2025)
- [Bidirectional Temporal Information Propagation for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2508.15415/) (2025)
- [You Only Look Omni Gradient Backpropagation for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2511.13013/) (2025)
- [MI-DETR: A Strong Baseline for Moving Infrared Small Target Detection with Motion Integration](https://mlsanity.com/paper/2603.05071/) (2026)
- [Cross-Scenario Deraining Adaptation with Unpaired Data: Superpixel Structural Priors and Multi-Stage Pseudo-Rain Synthesis](https://mlsanity.com/paper/2603.21661/) (2026)
- [Diffuse to Detect: Bi-Level Sample Rebalancing with Pseudo-Label Diffusion for Point-Supervised Infrared Small-Target Detection](https://mlsanity.com/paper/2605.20766/) (2026)
- [Decoupled Motion Representation Learning for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2606.15286/) (2026)

## Abstract (arXiv)

Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. Recently, convolutional neural networks have achieved significant advantages in general object detection. With the development of Transformer, the scale of SIRST models is constantly increasing. Due to the limited training samples, performance has not been improved accordingly. The quality, quantity, and diversity of the infrared dataset are critical to the detection of small targets. To highlight this issue, we propose a negative sample augmentation method in this paper. Specifically, a negative augmentation approach is proposed to generate massive negatives for self-supervised learning. Firstly, we perform a sequential noise modeling technology to generate realistic infrared data. Secondly, we fuse the extracted noise with the original data to facilitate diversity and fidelity in the generated data. Lastly, we proposed a negative augmentation strategy to enrich diversity as well as maintain semantic invariance. The proposed algorithm produces a synthetic SIRST-5K dataset, which contains massive pseudo-data and corresponding labels. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed. Compared with other state-of-the-art (SOTA) methods, our method achieves outstanding performance in terms of probability of detection (Pd), false-alarm rate (Fa), and intersection over union (IoU).