# Image Coding for Machines with Object Region Learning

arXiv: https://arxiv.org/abs/2308.13984 · HTML: https://mlsanity.com/paper/2308.13984/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 41 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [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)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [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)
- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [Full Resolution Image Compression with Recurrent Neural Networks](https://mlsanity.com/paper/1608.05148/) (2016)
- [The Devil Is in the Details: Window-based Attention for Image Compression](https://mlsanity.com/paper/2203.08450/) (2022)
- [Enhanced Invertible Encoding for Learned Image Compression](https://mlsanity.com/paper/2108.03690/) (2021)
- [Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics](https://mlsanity.com/paper/2001.03569/) (2020)
- [Transformer-based Image Compression](https://mlsanity.com/paper/2111.06707/) (2021)
- [Joint Global and Local Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/2112.04487/) (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)
- [End-to-end Compression Towards Machine Vision: Network Architecture Design and Optimization](https://mlsanity.com/paper/2107.00328/) (2021)

## Built on by (most cited first, 7 of 7)

- [Image Coding for Machines with Edge Information Learning Using Segment Anything](https://mlsanity.com/paper/2403.04173/) (2024)
- [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)
- [Delta-ICM: Entropy Modeling with Delta Function for Learned Image Compression](https://mlsanity.com/paper/2410.07669/) (2024)
- [Explicit Residual-Based Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2506.19297/) (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)

## Abstract (arXiv)

Compression technology is essential for efficient image transmission and storage. With the rapid advances in deep learning, images are beginning to be used for image recognition as well as for human vision. For this reason, research has been conducted on image coding for image recognition, and this field is called Image Coding for Machines (ICM). There are two main approaches in ICM: the ROI-based approach and the task-loss-based approach. The former approach has the problem of requiring an ROI-map as input in addition to the input image. The latter approach has the problems of difficulty in learning the task-loss, and lack of robustness because the specific image recognition model is used to compute the loss function. To solve these problems, we propose an image compression model that learns object regions. Our model does not require additional information as input, such as an ROI-map, and does not use task-loss. Therefore, it is possible to compress images for various image recognition models. In the experiments, we demonstrate the versatility of the proposed method by using three different image recognition models and three different datasets. In addition, we verify the effectiveness of our model by comparing it with previous methods.