# Image Coding for Machines with Edge Information Learning Using Segment Anything arXiv: https://arxiv.org/abs/2403.04173 ยท HTML: https://mlsanity.com/paper/2403.04173/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 29 references that are in the map. - First posted on arXiv 7 Mar 2024. ## Builds on (most cited first, 21 of 21) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017) - [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015) - [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016) - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022) - [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020) - [Panoptic-DeepLab](https://mlsanity.com/paper/1910.04751/) (2019) - [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020) - [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021) - [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023) - [Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2107.08373/) (2021) - [Image coding for machines: an end-to-end learned approach](https://mlsanity.com/paper/2108.09993/) (2021) - [Image Coding for Machines with Omnipotent Feature Learning](https://mlsanity.com/paper/2207.01932/) (2022) - [High efficiency compression for object detection](https://mlsanity.com/paper/1710.11151/) (2017) - [Visual Analysis Motivated Rate-Distortion Model for Image Coding](https://mlsanity.com/paper/2104.10315/) (2021) - [Learned Image Coding for Machines: A Content-Adaptive Approach](https://mlsanity.com/paper/2108.09992/) (2021) - [Learned Image Compression for Machine Perception](https://mlsanity.com/paper/2111.02249/) (2021) - [ROI-based Deep Image Compression with Swin Transformers](https://mlsanity.com/paper/2305.07783/) (2023) - [Image Coding for Machines with Object Region Learning](https://mlsanity.com/paper/2308.13984/) (2023) ## Built on by (most cited first, 17 of 17) - [Scalable Image Coding for Humans and Machines Using Feature Fusion Network](https://mlsanity.com/paper/2405.09152/) (2024) - [Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing](https://mlsanity.com/paper/2405.11894/) (2024) - [Unified Coding for Both Human Perception and Generalized Machine Analytics with CLIP Supervision](https://mlsanity.com/paper/2501.04579/) (2025) - [Compression Beyond Pixels: Semantic Compression with Multimodal Foundation Models](https://mlsanity.com/paper/2509.05925/) (2025) - [Delta-ICM: Entropy Modeling with Delta Function for Learned Image Compression](https://mlsanity.com/paper/2410.07669/) (2024) - [Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines](https://mlsanity.com/paper/2503.19278/) (2025) - [Explicit Residual-Based Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2506.19297/) (2025) - [SLIM: Semantic-based Low-bitrate Image compression for Machines by leveraging diffusion](https://mlsanity.com/paper/2512.18200/) (2025) - [Guided Diffusion for the Extension of Machine Vision to Human Visual Perception](https://mlsanity.com/paper/2503.17907/) (2025) - [Training-Free Adaptive Quantization for Variable Rate Image Coding for Machines](https://mlsanity.com/paper/2511.05836/) (2025) - [Progressive Learned Image Compression for Machine Perception](https://mlsanity.com/paper/2512.20070/) (2025) - [Learned Image Compression for Vision-Language-Action Models](https://mlsanity.com/paper/2606.16253/) (2026) - [Seed Selection for Human-Oriented Image Reconstruction via Guided Diffusion](https://mlsanity.com/paper/2506.05363/) (2025) - [CI-ICM: Channel Importance-driven Learned Image Coding for Machines](https://mlsanity.com/paper/2604.05347/) (2026) - [Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification](https://mlsanity.com/paper/2605.08266/) (2026) - [Training-Free Continuous Bitrate Control for Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2606.00158/) (2026) - [You've Seen Enough: Quality-Constrained Image Coding for Machines](https://mlsanity.com/paper/2609.25108/) (2026) ## Abstract (arXiv) Image Coding for Machines (ICM) is an image compression technique for image recognition. This technique is essential due to the growing demand for image recognition AI. In this paper, we propose a method for ICM that focuses on encoding and decoding only the edge information of object parts in an image, which we call SA-ICM. This is an Learned Image Compression (LIC) model trained using edge information created by Segment Anything. Our method can be used for image recognition models with various tasks. SA-ICM is also robust to changes in input data, making it effective for a variety of use cases. Additionally, our method provides benefits from a privacy point of view, as it removes human facial information on the encoder's side, thus protecting one's privacy. Furthermore, this LIC model training method can be used to train Neural Representations for Videos (NeRV), which is a video compression model. By training NeRV using edge information created by Segment Anything, it is possible to create a NeRV that is effective for image recognition (SA-NeRV). Experimental results confirm the advantages of SA-ICM, presenting the best performance in image compression for image recognition. We also show that SA-NeRV is superior to ordinary NeRV in video compression for machines. Code is available at https://github.com/final-0/SA-ICM.