# Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification

arXiv: https://arxiv.org/abs/2605.08266 · HTML: https://mlsanity.com/paper/2605.08266/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (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)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [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)
- [TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception](https://mlsanity.com/paper/2306.05085/) (2023)
- [Image Compression for Machine and Human Vision with Spatial-Frequency Adaptation](https://mlsanity.com/paper/2407.09853/) (2024)
- [DPICT: Deep Progressive Image Compression Using Trit-Planes](https://mlsanity.com/paper/2112.06334/) (2021)
- [Progressive Neural Image Compression with Nested Quantization and Latent Ordering](https://mlsanity.com/paper/2102.02913/) (2021)
- [Context-Based Trit-Plane Coding for Progressive Image Compression](https://mlsanity.com/paper/2303.05715/) (2023)
- [Image Coding for Machines with Edge Information Learning Using Segment Anything](https://mlsanity.com/paper/2403.04173/) (2024)
- [Visually Consistent Hierarchical Image Classification](https://mlsanity.com/paper/2406.11608/) (2024)
- [All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation](https://mlsanity.com/paper/2409.19660/) (2024)
- [ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training](https://mlsanity.com/paper/2305.02145/) (2023)
- [DeepHQ: Learned Hierarchical Quantizer for Progressive Deep Image Coding](https://mlsanity.com/paper/2408.12150/) (2024)
- [Efficient Progressive Image Compression with Variance-aware Masking](https://mlsanity.com/paper/2411.10185/) (2024)
- [SLIM: Semantic-based Low-bitrate Image compression for Machines by leveraging diffusion](https://mlsanity.com/paper/2512.18200/) (2025)
- [LimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & Networks](https://mlsanity.com/paper/2504.13736/) (2025)
- [Progressive Learned Image Compression for Machine Perception](https://mlsanity.com/paper/2512.20070/) (2025)

## Abstract (arXiv)

Recent advances in learned image compression (LIC) have enabled practical deployments, spurring active research into image compression for machines and progressive coding schemes. However, their integration remains under-explored: prior works on progressive machine codec predominantly target sample-level difficulty adaptation (i.e., easy-to-hard), without considering semantic-level scalability. In this work, we introduce a semantic hierarchy-aware progressive codec that enables semantic scalability (i.e., coarse-to-fine) from a single bitstream. We first systematically categorize ImageNet-1K classes into CLIP embedding-based semantic hierarchies. Based on a channel-wise autoregressive framework, we decompose latent representations into hierarchically ordered channel blocks, each explicitly optimized for a corresponding semantic hierarchy. Extensive experiments demonstrate that our approach substantially improves coarse-level recognition at low bitrates while maintaining fine-grained accuracy at higher bitrates. By reframing progressive transmission through the lens of semantic scalability, our work provides an efficient and interpretable solution for task-adaptive image coding, outperforming existing progressive codecs under hierarchical evaluation.