# Hybrid Mask Generation for Infrared Small Target Detection with Single-Point Supervision

arXiv: https://arxiv.org/abs/2409.04011 · HTML: https://mlsanity.com/paper/2409.04011/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 42 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Weakly Supervised Deep Detection Networks](https://mlsanity.com/paper/1511.02853/) (2015)
- [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)
- [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020)
- [Attentional Local Contrast Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2012.08573/) (2020)
- [Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks](https://mlsanity.com/paper/2108.07478/) (2021)
- [Fully Convolutional Networks for Panoptic Segmentation](https://mlsanity.com/paper/2012.00720/) (2020)
- [Pointly-Supervised Instance Segmentation](https://mlsanity.com/paper/2104.06404/) (2021)
- [STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/1509.03150/) (2015)
- [Infrared Small Target Detection with Scale and Location Sensitivity](https://mlsanity.com/paper/2403.19366/) (2024)
- [SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection](https://mlsanity.com/paper/2401.15583/) (2024)
- [Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision](https://mlsanity.com/paper/2304.01484/) (2023)
- [Weakly Supervised Scene Parsing with Point-based Distance Metric Learning](https://mlsanity.com/paper/1811.02233/) (2018)
- [Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2310.19001/) (2023)
- [Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection](https://mlsanity.com/paper/2304.04442/) (2023)
- [EFLNet: Enhancing Feature Learning for Infrared Small Target Detection](https://mlsanity.com/paper/2307.14723/) (2023)
- [TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection](https://mlsanity.com/paper/2402.02046/) (2024)
- [Beyond Full Labels: Energy-Double-Guided Single-Point Prompt for Infrared Small Target Label Generation](https://mlsanity.com/paper/2408.08191/) (2024)
- [The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results](https://mlsanity.com/paper/2408.09615/) (2024)

## Built on by (most cited first, 3 of 3)

- [Point-to-Mask: From Arbitrary Point Annotations to Mask-Level Infrared Small Target Detection](https://mlsanity.com/paper/2603.16257/) (2026)
- [Rethinking IRSTD: Single-Point Supervision Guided Encoder-only Framework is Enough for Infrared Small Target Detection](https://mlsanity.com/paper/2604.05363/) (2026)
- [Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization](https://mlsanity.com/paper/2607.17148/) (2026)

## Abstract (arXiv)

Single-frame infrared small target (SIRST) detection poses a significant challenge due to the requirement to discern minute targets amidst complex infrared background clutter. In this paper, we focus on a weakly-supervised paradigm to obtain high-quality pseudo masks from the point-level annotation by integrating a novel learning-free method with the hybrid of the learning-based method. The learning-free method adheres to a sequential process, progressing from a point annotation to the bounding box that encompasses the target, and subsequently to detailed pseudo masks, while the hybrid is achieved through filtering out false alarms and retrieving missed detections in the network's prediction to provide a reliable supplement for learning-free masks. The experimental results show that our learning-free method generates pseudo masks with an average Intersection over Union (IoU) that is 4.3% higher than the second-best learning-free competitor across three datasets, while the hybrid learning-based method further enhances the quality of pseudo masks, achieving an additional average IoU increase of 3.4%.