ACNet: Mask-Aware Attention with Dynamic Context Enhancement for Robust Acne Detection
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection
Current state-of-the-art approaches for spatio-temporal action detection have achieved impressive results but remain unsatisfactory for temporal extent detection. The main reason comes from that, there are some ambiguous…
Action DetectionContextual Scene Augmentation and Synthesis via GSACNet
Indoor scene augmentation has become an emerging topic in the field of computer vision and graphics with applications in augmented and virtual reality. However, current state-of-the-art systems using deep neural networks…
Data AugmentationGraph AttentionHSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection
Semi-supervised change detection (SSCD) aims to detect changes between bi-temporal remote sensing images by utilizing limited labeled data and abundant unlabeled data. Existing methods struggle in complex scenarios, exhi…
Change Detectionparameter-efficient fine-tuningSemi-supervised Change DetectionXMACNet: An Explainable Lightweight Attention based CNN with Multi Modal Fusion for Chili Disease Classification
Plant disease classification via imaging is a critical task in precision agriculture. We propose XMACNet, a novel light-weight Convolutional Neural Network (CNN) that integrates self-attention and multi-modal fusion of v…
Anatomy Aware Cascade Network: Bridging Epistemic Uncertainty and Geometric Manifold for 3D Tooth Segmentation
Accurate three-dimensional (3D) tooth segmentation from Cone-Beam Computed Tomography (CBCT) is a prerequisite for digital dental workflows. However, achieving high-fidelity segmentation remains challenging due to adhesi…