# Privacy-Preserving Semantic Segmentation from Ultra-Low-Resolution RGB Inputs

arXiv: https://arxiv.org/abs/2507.16034 · HTML: https://mlsanity.com/paper/2507.16034/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 50 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 30 of 35)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One](https://mlsanity.com/paper/2312.06709/) (2023)
- [You Only Need Adversarial Supervision for Semantic Image Synthesis](https://mlsanity.com/paper/2012.04781/) (2020)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation](https://mlsanity.com/paper/2405.06880/) (2024)
- [Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images](https://mlsanity.com/paper/1712.01066/) (2017)
- [DFormer: Diffusion-guided Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2306.03437/) (2023)
- [Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels](https://mlsanity.com/paper/2409.19846/) (2024)
- [Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning](https://mlsanity.com/paper/1708.00999/) (2017)
- [Improving Bird's Eye View Semantic Segmentation by Task Decomposition](https://mlsanity.com/paper/2404.01925/) (2024)
- [Privacy-Aware Visual Language Models](https://mlsanity.com/paper/2405.17423/) (2024)
- [PrivCirNet: Efficient Private Inference via Block Circulant Transformation](https://mlsanity.com/paper/2405.14569/) (2024)
- [Anonymization for Skeleton Action Recognition](https://mlsanity.com/paper/2111.15129/) (2021)
- [Semantically Accurate Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/2205.08659/) (2022)
- [MOSAIC: Generating Consistent, Privacy-Preserving Scenes from Multiple Depth Views in Multi-Room Environments](https://mlsanity.com/paper/2503.13816/) (2025)
- [ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition](https://mlsanity.com/paper/2003.05328/) (2020)

## Built on by (most cited first, 1 of 1)

- [Privacy-Preserving Semantic Segmentation from High-Resolution Depth and Ultra-Low-Resolution RGB](https://mlsanity.com/paper/2609.28360/) (2026)

## Abstract (arXiv)

RGB-based semantic segmentation has become a mainstream approach for visual perception and is widely applied in a variety of downstream tasks. However, existing methods typically rely on high-resolution RGB inputs, which may expose sensitive visual content in privacy-critical environments. Ultra-low-resolution RGB sensing suppresses sensitive information directly during image acquisition, making it an attractive privacy-preserving alternative. Nevertheless, recovering semantic segmentation from ultra-low-resolution RGB inputs remains highly challenging due to severe visual degradation. In this work, we introduce a novel fully joint-learning framework to mitigate the optimization conflicts exacerbated by visual degradation for ultra-low-resolution semantic segmentation. Experiments demonstrate that our method outperforms representative baselines in semantic segmentation performance and our ultra-low-resolution RGB input achieves a favorable trade-off between privacy preservation and semantic segmentation performance. We deploy our privacy-preserving semantic segmentation method in a real-world robotic object-goal navigation task, demonstrating successful downstream task execution even under severe visual degradation.