masteren's repositories
Chinese_traffic_signs_classify
基于 ResNet 的国内交通信号图标分类
real_url_GUI
基于real_url的图形化直播源获取
Bilinear-Matching-Network
Official implementation for CVPR 2022 paper "Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting".
Boosting-Crowd-Counting-via-Multifaceted-Attention
Official Implement of CVPR 2022 paper 'Boosting Crowd Counting via Multifaceted Attention'
CLTR
An End-to-End Transformer Model for Crowd Localization [ECCV 2022]
Context-Aware-Crowd-Counting
Official Code for Context-Aware Crowd Counting. CVPR 2019
crowdcount-mcnn
Single Image Crowd Counting via MCNN (Unofficial Implementation)
CrowdCounting-P2PNet
The official codes for the ICCV2021 Oral presentation "Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework"
CrowdCounting-SASNet
Official implementation in PyTorch of SASNet as described in "To Choose or to Fuse? Scale Selection for Crowd Counting"
CSRNet-pytorch
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
DM-Count
Code for NeurIPS 2020 paper: Distribution Matching for Crowd Counting.
DRNet
PyTorch implementations of the paper: "DR.VIC: Decomposition and Reasoning for Video Individual Counting, CVPR, 2022"
DroneCrowd
Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network
FIDTM
Focal Inverse Distance Transform Maps for Crowd Localization [IEEE TMM]
GitHubGraduation-2022
Join the GitHub Graduation Yearbook and "walk the stage" on June 11.
masImageSyn
image bed
qBittorrent-NAT-TCP-Hole-Punching
qBittorrent NAT Hole Punching/qBittorrent NAT 打洞
Rethinking-Counting
Code for "Rethinking Spatial Invariance of Convolutional Networks for Object Counting," CVPR 2022
SAFECount
[WACV 2023] Few-shot Object Counting with Similarity-Aware Feature Enhancement
sganet-crowd-counting
A Pytorch implementation of SGANet for crowd counting
Variations-of-SFANet-for-Crowd-Counting
The official implementation of "Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting"