홈 › Papers

ACLNet: An Attention and Clustering-based Cloud Segmentation Network

2022-07-13 · Dhruv Makwana, Subhrajit Nag, Onkar Susladkar, Gayatri Deshmukh, Sai Chandra Teja R, Sparsh Mittal, C Krishna Mohan

We propose a novel deep learning model named ACLNet, for cloud segmentation from ground images. ACLNet uses both deep neural network and machine learning (ML) algorithm to extract complementary features. Specifically, it uses EfficientNet-B0 as the backbone, "`a trous spatial pyramid pooling" (ASPP) to learn at multiple receptive fields, and "global attention module" (GAM) to extract finegrained details from the image. ACLNet also uses k-means clustering to extract cloud boundaries more precisely. ACLNet is effective for both daytime and nighttime images. It provides lower error rate, higher recall and higher F1-score than state-of-art cloud segmentation models. The source-code of ACLNet is available here: https://github.com/ckmvigil/ACLNet.

📄 PDF Abstract BibTeX arXiv:2207.06277

Code (1)

ckmvigil/aclnet 공식 구현 tf

Tasks

ClusteringSegmentation Semantic Segmentation

Methods 이 논문이 사용한 방법론

k-Means Clustering k-Means Clustering is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$…

PointDC:Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering

2023-04-18 · Zisheng Chen, Hongbin Xu, Weitao Chen, Zhipeng Zhou 외

Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations. In this…

Clustering Segmentation Semantic Segmentation Unsupervised Semantic Segmentation

PointDC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-Modal Distillation and Super-Voxel Clustering

2023-01-01 · ICCV 2023 1 · Zisheng Chen, Hongbin Xu, Weitao Chen, Zhipeng Zhou 외

Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to a challenging topic of learning from unlabeled or weaker form of annotations. In thi…

Clustering Segmentation Semantic Segmentation Unsupervised Semantic Segmentation

Affinity Contrastive Learning for Skeleton-based Human Activity Understanding

2026-01-23 · Hongda Liu, Yunfan Liu, Min Ren, Lin Sui 외 arxiv

In skeleton-based human activity understanding, existing methods often adopt the contrastive learning paradigm to construct a discriminative feature space. However, many of these approaches fail to exploit the structural…

Person Re-Identification Contrastive Learning Action Recognition Gait Recognition

Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation

2024-03-03 · Dipesh Gyawali, Jian Zhang, BB Karki

Point cloud segmentation, which helps us understand the environment of specific structures and objects, can be performed in class-specific and class-agnostic ways. We propose a novel region-based transformer model called…

Autonomous Vehicles Point Cloud Segmentation Segmentation

An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds

2017-03-06 · Sheng Xu, Ruisheng Wang, Han Zheng

This paper proposes a hierarchical clustering approach for the segmentation of mobile LiDAR point clouds. We perform the hierarchical clustering on unorganized point clouds based on a proximity matrix. The dissimilarity …

Clustering Point Cloud Segmentation Segmentation