# Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity

arXiv: https://arxiv.org/abs/2607.03754 · HTML: https://mlsanity.com/paper/2607.03754/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 32 references that are in the map.
- First posted on arXiv 4 Jul 2026.

## Builds on (most cited first, 24 of 24)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Self-supervised Augmentation Consistency for Adapting Semantic Segmentation](https://mlsanity.com/paper/2105.00097/) (2021)
- [MetaFormer Baselines for Vision](https://mlsanity.com/paper/2210.13452/) (2022)
- [Self-Supervised Learning of Object Parts for Semantic Segmentation](https://mlsanity.com/paper/2204.13101/) (2022)
- [Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2305.05803/) (2023)
- [Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2305.01275/) (2023)
- [mathrmSAMMed: A medical image annotation framework based on large vision model](https://mlsanity.com/paper/2307.05617/) (2023)
- [A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing](https://mlsanity.com/paper/2202.04757/) (2022)
- [Distilled Pooling Transformer Encoder for Efficient Realistic Image Dehazing](https://mlsanity.com/paper/2412.14220/) (2024)
- [Unpaired Image Dehazing via Kolmogorov-Arnold Transformation of Latent Features](https://mlsanity.com/paper/2502.07812/) (2025)

## Abstract (arXiv)

Unmanned aerial vehicle (UAV) target segmentation remains challenging due to the small size of objects, appearance variations, cluttered backgrounds, and the scarcity of densely annotated data. These factors hinder the performance and practical deployment of lightweight segmentation models in real-world UAV applications. To address this problem, this paper investigates the use of SAM3 (Segment Anything Model 3) as a pseudo-label generator for training compact segmentation networks. Specifically, two supervision paradigms are explored: (i) direct pseudo-supervision using unaltered SAM3-generated masks, and (ii) a refinement strategy that re-applies SAM3 to localized image patches for improved mask quality. Based on these paradigms, a two-stage SAM3-guided pseudo-label generation framework is proposed. In the first stage, SAM3 generates coarse masks for initial object localization. The localized regions are subsequently cropped into patches and processed by SAM3 again to generate fine masks with accurate object boundaries and discard false positives. The resulting coarse and fine masks are then used as pseudo-labels to optimize a lightweight network, termed IPS-Seg, which consists of three components: an IdentityFormer backbone for feature extraction, an Atrous Spatial Pyramid Pooling module for multi-scale context aggregation, and a PixelShuffle-based decoder for spatial resolution recovery. Extensive experiments under multiple supervision settings demonstrate the effectiveness of the proposed framework. The results show that IPS-Seg achieves a favorable trade-off between segmentation accuracy and computational efficiency while benefiting consistently from the proposed pseudo-label generation strategy. These findings highlight the potential of large-scale foundation models as annotation sources for training compact task-specific segmentation networks in low-label vision domains.