# Texture-guided Coding for Deep Features

arXiv: https://arxiv.org/abs/2405.19669 · HTML: https://mlsanity.com/paper/2405.19669/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018)
- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2107.08373/) (2021)
- [Deep feature compression for collaborative object detection](https://mlsanity.com/paper/1802.03931/) (2018)
- [Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression](https://mlsanity.com/paper/2004.10043/) (2020)
- [BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing](https://mlsanity.com/paper/2201.02693/) (2022)
- [Near-Lossless Deep Feature Compression for Collaborative Intelligence](https://mlsanity.com/paper/1804.09963/) (2018)
- [Feature Compression for Rate Constrained Object Detection on the Edge](https://mlsanity.com/paper/2204.07314/) (2022)

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

- [Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior](https://mlsanity.com/paper/2511.22549/) (2025)

## Abstract (arXiv)

With the rapid development of machine vision technology in recent years, many researchers have begun to focus on feature compression that is better suited for machine vision tasks. The target of feature compression is deep features, which arise from convolution in the middle layer of a pre-trained convolutional neural network. However, due to the large volume of data and high level of abstraction of deep features, their application is primarily limited to machine-centric scenarios, which poses significant constraints in situations requiring human-computer interaction. This paper investigates features and textures and proposes a texture-guided feature compression strategy based on their characteristics. Specifically, the strategy comprises feature layers and texture layers. The feature layers serve the machine, including a feature selection module and a feature reconstruction network. With the assistance of texture images, they selectively compress and transmit channels relevant to visual tasks, reducing feature data while providing high-quality features for the machine. The texture layers primarily serve humans and consist of an image reconstruction network. This image reconstruction network leverages features and texture images to reconstruct preview images for humans. Our method fully exploits the characteristics of texture and features. It eliminates feature redundancy, reconstructs high-quality preview images for humans, and supports decision-making. The experimental results demonstrate excellent performance when employing our proposed method to compress the deep features.