# Compressed Feature Quality Assessment: Dataset and Baselines

arXiv: https://arxiv.org/abs/2506.07412 · HTML: https://mlsanity.com/paper/2506.07412/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 9 Jun 2025.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics](https://mlsanity.com/paper/2001.03569/) (2020)
- [End-to-end Learning of Compressible Features](https://mlsanity.com/paper/2007.11797/) (2020)
- [Image Coding for Machines with Omnipotent Feature Learning](https://mlsanity.com/paper/2207.01932/) (2022)
- [Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression](https://mlsanity.com/paper/2004.10043/) (2020)
- [Multi-task learning with compressible features for Collaborative Intelligence](https://mlsanity.com/paper/1902.05179/) (2019)
- [Pareto-Optimal Bit Allocation for Collaborative Intelligence](https://mlsanity.com/paper/2009.12430/) (2020)
- [Latent-space scalability for multi-task collaborative intelligence](https://mlsanity.com/paper/2105.10089/) (2021)
- [Bit Allocation for Multi-Task Collaborative Intelligence](https://mlsanity.com/paper/2002.07048/) (2020)
- [End-to-End Learnable Multi-Scale Feature Compression for VCM](https://mlsanity.com/paper/2306.16670/) (2023)
- [Feature Coding in the Era of Large Models: Dataset, Test Conditions, and Benchmark](https://mlsanity.com/paper/2412.04307/) (2024)
- [Feature Compression for Rate Constrained Object Detection on the Edge](https://mlsanity.com/paper/2204.07314/) (2022)
- [DMOFC: Discrimination Metric-Optimized Feature Compression](https://mlsanity.com/paper/2405.04044/) (2024)
- [Toward Scalable Image Feature Compression: A Content-Adaptive and Diffusion-Based Approach](https://mlsanity.com/paper/2410.06149/) (2024)

## Built on by (most cited first, 3 of 3)

- [Cross-architecture universal feature coding via distribution alignment](https://mlsanity.com/paper/2506.12737/) (2025)
- [Visual Token Codec: Unleashing Spatial Redundancy for ViT Feature Coding](https://mlsanity.com/paper/2608.08832/) (2026)
- [Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models](https://mlsanity.com/paper/2608.21247/) (2026)

## Abstract (arXiv)

The widespread deployment of large models in resource-constrained environments has underscored the need for efficient transmission of intermediate feature representations. In this context, feature coding, which compresses features into compact bitstreams, becomes a critical component for scenarios involving feature transmission, storage, and reuse. However, this compression process inevitably introduces semantic degradation that is difficult to quantify with traditional metrics. To address this, we formalize the research problem of Compressed Feature Quality Assessment (CFQA), aiming to evaluate the semantic fidelity of compressed features. To advance CFQA research, we propose the first benchmark dataset, comprising 300 original features and 12000 compressed features derived from three vision tasks and four feature codecs. Task-specific performance degradation is provided as true semantic distortion for evaluating CFQA metrics. We systematically assess three widely used metrics -- MSE, cosine similarity, and Centered Kernel Alignment (CKA) -- in terms of their ability to capture semantic degradation. Our findings demonstrate the representativeness of the proposed dataset while underscoring the need for more sophisticated metrics capable of measuring semantic distortion in compressed features. This work advances the field by establishing a foundational benchmark and providing a critical resource for the community to explore CFQA. To foster further research, we release the dataset and all associated source code at https://github.com/chansongoal/Compressed-Feature-Quality-Assessment.