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Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision

Pytorch implementation of our Label Evolution with Single Point Supervision (LESPS). [Paper]   [Web] <br>

News: We recommend our newly-released repository BasicIRSTD, an open-source and easy-to-use toolbox for infrared small target detction. [link] <br><br>

Overview

<img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/LESPS_presentation.png" width="800"/><br> Sclice is in Figs/LESPS_presentation.pptx

The Mapping Degeneration Phenomenon

<img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/MD1.jpg" width="550"/><br> Fig. 1. Illustrations of mapping degeneration under point supervision. CNNs always tend to segment a cluster of pixels near the targets with low confidence at the early stage, and then gradually learn to predict groundtruth point labels with high confidence.<br>

<img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/MD2.jpg" width="550"/><br> Fig. 2. Quantitative and qualitative illustrations of mapping degeneration in CNNs.<br><br>

The Label Evolution Framework

<img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/LESPS.jpg" width="550"/><br> Fig. 3. Illustrations of Label Evolution with Single Point Supervision (LESPS). During training, intermediate predictions of CNNs are used to progressively expand point labels to mask labels. Black arrows represent each round of label updates.<br><br>

Requirements

Datasets

SIRST3 is used for training, and is a combination of NUAA-SIRST, NUDT-SIRST, IRSTD-1K datasets. Please first download datasets via Baidu Drive (key:1113), and place the datasets to the folder ./datasets/.

To gnenrate centroid annoation, run matlab code centroid_anno.m

To gnenrate coarse annoation, run matlab code coarse_anno.m

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Train

python train.py --model_names DNANet ALCNet ACM --dataset_names SIRST3 --label_type 'centroid'

python train.py --model_names DNANet ALCNet ACM --dataset_names SIRST3 --label_type 'coarse'

python train_full.py --model_names DNANet ALCNet ACM --dataset_names SIRST3 --label_type 'full'
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Test

python test.py --model_names DNANet ALCNet ACM --pth_dirs None --dataset_names NUAA-SIRST NUDT-SIRST IRSTD-1K

python test.py --model_names DNANet ALCNet ACM --pth_dirs SIRST3/DNANet_full.pth.tar SIRST3/DNANet_LESPS_centroid.pth.tar SIRST3/DNANet_LESPS_coarse.pth.tar SIRST3/ALCNet_full.pth.tar SIRST3/ALCNet_LESPS_centroid.pth.tar SIRST3/ALCNet_LESPS_coarse.pth.tar SIRST3/ACM_full.pth.tar SIRST3/ACM_LESPS_centroid.pth.tar SIRST3/ACM_LESPS_coarse.pth.tar --dataset_names NUAA-SIRST NUDT-SIRST IRSTD-1K
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Model Analyses

Analyses of Mapping Degeneration

<img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/MD_abl.jpg" width="800"/><br> Fig. 4. IoU and visualize results of mapping degeneration with respect to different characteristics of targets (i.e.,(a) intensity, (b) size, (c) shape, and (d) local background clutter) and point labels (i.e.,(e) numbers and (f) locations). We visualize the zoom-in target regions of input images with GT point labels (i.e., red dots in images) and corresponding CNN predictions (in the epoch reaching maximum IoU).<br><br>

Analyses of the Label Evolution Framework

Effectiveness<br>

Table 1. Average results achieved by DNAnet with (w/) and without (w/o) LESPS under centroid, coarse point supervision together with full supervision.<br> <img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/table0.jpg" width="450" /><br>

<img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/LESPS_exp.jpg" width="500"/><br> Fig. 5. Quantitative and qualitative results of evolved target masks.<br>

<img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/visual1.jpg" width="500"/><br> Fig. 6. Visualizations of regressed labels during training and network predictions during inference with centroid and coarse point supervision.<br>

Parameters

<img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/LESPS_abl.jpg" width="800"/><br> Fig. 7. PA (P) and IoU (I) results of LESPS with respect to (a) initial evolution epoch, (b) Tb and (c) k of evolution threshold, and (d) evolution frequency.<br><br>

Comparison Results

Comparison to SISRT Detection Methods

Table 2. Results of different methods. “CNN Full”, “CNN Centroid”, and “CNN Coarse” represent CNN-based methods under full supervision, centroid and coarse point supervision. “+” represents CNN-based methods equipped with LESPS.<br> <img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/table1.jpg" width="1000"/><br>

<img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/visual2.jpg" width="1000"/><br> Fig. 8. Visual detection results of different methods. Correctly detected targets and false alarms are highlighted by red and orange circles, respectively.<br><br>

Comparison to Fixed Pseudo Labels

Table 3. Results of DNA-Net trained with pseudo labels generated by input intensity threshold, LCM-based methods and LESPS under centroid and coarse point supervision. Best results are shown in boldface.<br> <img src="https://raw.github.com/XinyiYing/LESPS/master/Figs/table2.jpg" width="450"/><br><br>

Citiation

@article{LESPS,
  author = {Ying, Xinyi and Liu, Li and Wang, Yingqian and Li, Ruojing and Chen, Nuo and Lin, Zaiping and Sheng, Weidong and Zhou, Shilin},
  title = {Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision},
  journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
}
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Contact

Welcome to raise issues or email to yingxinyi18@nudt.edu.cn for any question.