# Image Coding for Machines with Omnipotent Feature Learning

arXiv: https://arxiv.org/abs/2207.01932 · HTML: https://mlsanity.com/paper/2207.01932/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 87 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 30 of 66)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [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)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)

## Built on by (most cited first, 30 of 31)

- [Image Compression for Machine and Human Vision with Spatial-Frequency Adaptation](https://mlsanity.com/paper/2407.09853/) (2024)
- [Image Coding for Machines with Edge Information Learning Using Segment Anything](https://mlsanity.com/paper/2403.04173/) (2024)
- [All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation](https://mlsanity.com/paper/2409.19660/) (2024)
- [Semantically Structured Image Compression via Irregular Group-Based Decoupling](https://mlsanity.com/paper/2305.02586/) (2023)
- [SMC++: Masked Learning of Unsupervised Video Semantic Compression](https://mlsanity.com/paper/2406.04765/) (2024)
- [Rate-Distortion-Cognition Controllable Versatile Neural Image Compression](https://mlsanity.com/paper/2407.11700/) (2024)
- [Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling](https://mlsanity.com/paper/2211.06797/) (2022)
- [Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts](https://mlsanity.com/paper/2305.02578/) (2023)
- [Image Coding for Machines with Object Region Learning](https://mlsanity.com/paper/2308.13984/) (2023)
- [Task-Aware Encoder Control for Deep Video Compression](https://mlsanity.com/paper/2404.04848/) (2024)
- [Scalable Image Coding for Humans and Machines Using Feature Fusion Network](https://mlsanity.com/paper/2405.09152/) (2024)
- [DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation](https://mlsanity.com/paper/2506.16495/) (2025)
- [Distributed Semantic Segmentation with Efficient Joint Source and Task Decoding](https://mlsanity.com/paper/2407.11224/) (2024)
- [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)
- [Compressed Feature Quality Assessment: Dataset and Baselines](https://mlsanity.com/paper/2506.07412/) (2025)
- [Explicit Residual-Based Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2506.19297/) (2025)
- [When MLLMs Meet Compression Distortion: A Coding Paradigm Tailored to MLLMs](https://mlsanity.com/paper/2509.24258/) (2025)
- [Just Noticeable Difference Modeling for Deep Visual Features](https://mlsanity.com/paper/2601.21933/) (2026)
- [DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines](https://mlsanity.com/paper/2411.09308/) (2024)
- [All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception](https://mlsanity.com/paper/2504.12997/) (2025)
- [Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference](https://mlsanity.com/paper/2507.01608/) (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)
- [Lossy Common Information in a Learnable Gray-Wyner Network](https://mlsanity.com/paper/2601.21424/) (2026)
- [Towards Large Model Feature Coding](https://mlsanity.com/paper/2605.24025/) (2026)
- [Efficient Masked Image Compression with Position-Indexed Self-Attention](https://mlsanity.com/paper/2504.12923/) (2025)
- [Comp-X: On Defining an Interactive Learned Image Compression Paradigm With Expert-driven LLM Agent](https://mlsanity.com/paper/2508.15243/) (2025)
- [MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts](https://mlsanity.com/paper/2606.21033/) (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) aims to compress images for AI tasks analysis rather than meeting human perception. Learning a kind of feature that is both general (for AI tasks) and compact (for compression) is pivotal for its success. In this paper, we attempt to develop an ICM framework by learning universal features while also considering compression. We name such features as omnipotent features and the corresponding framework as Omni-ICM. Considering self-supervised learning (SSL) improves feature generalization, we integrate it with the compression task into the Omni-ICM framework to learn omnipotent features. However, it is non-trivial to coordinate semantics modeling in SSL and redundancy removing in compression, so we design a novel information filtering (IF) module between them by co-optimization of instance distinguishment and entropy minimization to adaptively drop information that is weakly related to AI tasks (e.g., some texture redundancy). Different from previous task-specific solutions, Omni-ICM could directly support AI tasks analysis based on the learned omnipotent features without joint training or extra transformation. Albeit simple and intuitive, Omni-ICM significantly outperforms existing traditional and learning-based codecs on multiple fundamental vision tasks.