# ACNet: Mask-Aware Attention with Dynamic Context Enhancement for Robust Acne Detection

arXiv: https://arxiv.org/abs/2105.14891 · HTML: https://mlsanity.com/paper/2105.14891/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 14 of 14)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://mlsanity.com/paper/1904.11492/) (2019)
- [CBNet: A Novel Composite Backbone Network Architecture for Object Detection](https://mlsanity.com/paper/1909.03625/) (2019)
- [Few-Shot Object Detection via Knowledge Transfer](https://mlsanity.com/paper/2008.12496/) (2020)
- [An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network](https://mlsanity.com/paper/1711.04481/) (2017)
- [Object Detection of Satellite Images Using Multi-Channel Higher-order Local Autocorrelation](https://mlsanity.com/paper/1707.09099/) (2017)

## Built on by (most cited first, 5 of 5)

- [Automatic Facial Skin Feature Detection for Everyone](https://mlsanity.com/paper/2203.16056/) (2022)
- [Learning High-quality Proposals for Acne Detection](https://mlsanity.com/paper/2207.03674/) (2022)
- [Towards Accurate Acne Detection via Decoupled Sequential Detection Head](https://mlsanity.com/paper/2301.12219/) (2023)
- [CG-HAF: An Interpretable Global-Local Lesion-Burden Fusion Framework for Ordinal Acne Severity Grading in Agentic Skincare Support](https://mlsanity.com/paper/2609.31326/) (2026)
- [LENS-GRF: Permutation-Invariant Lesion Evidence Network with Gated Residual Fusion for Acne Severity Grading and Multi-Rater Clinical Oracle Analysis](https://mlsanity.com/paper/2610.00294/) (2026)

## Abstract (arXiv)

Computer-aided diagnosis has recently received attention for its advantage of low cost and time efficiency. Although deep learning played a major role in the recent success of acne detection, there are still several challenges such as color shift by inconsistent illumination, variation in scales, and high density distribution. To address these problems, we propose an acne detection network which consists of three components, specifically: Composite Feature Refinement, Dynamic Context Enhancement, and Mask-Aware Multi-Attention. First, Composite Feature Refinement integrates semantic information and fine details to enrich feature representation, which mitigates the adverse impact of imbalanced illumination. Then, Dynamic Context Enhancement controls different receptive fields of multi-scale features for context enhancement to handle scale variation. Finally, Mask-Aware Multi-Attention detects densely arranged and small acne by suppressing uninformative regions and highlighting probable acne regions. Experiments are performed on acne image dataset ACNE04 and natural image dataset PASCAL VOC 2007. We demonstrate how our method achieves the state-of-the-art result on ACNE04 and competitive performance with previous state-of-the-art methods on the PASCAL VOC 2007.