starstylesky,星纬,github

ccpd

[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition

centernet

Object detection, 3D detection, and pose estimation using center point detection:

centernet_pro_max

Experiments based on CenterNet (more backbones, TensorRT deployment and mask head)

cfenet

Comprehensive Feature Enhancement Module for Single-Shot Object Detector

chainercv

ChainerCV: a Library for Computer Vision in Deep Learning

chalearn_liveness_challenge

ChaLearn Face Anti-spoofing Attack Detection Challenge@CVPR2019

class-balanced-grouping-and-sampling-for-point-cloud-3d-object-detection

Winner's Code and Technical Report for the nuScenes 3D Object Detection challenge in WAD, CVPR 2019

cliquenet

Convolutional Neural Networks with Alternately Updated Clique (to appear in CVPR 2018)

cnn_face_detection

Implementation based on the paper Li et al., “A Convolutional Neural Network Cascade for Face Detection, ” 2015 CVPR

cnn_head_detection

Code for ICCV 2015 paper "Context-aware CNNs for person head detection"

coco-analyze

A wrapper of the COCOeval class for extended keypoint error estimation analysis.

coco_loss

Implement for ``Learning Deep Features via Congenerous Cosine Loss for Person Recognition''

codes-for-steering-control

Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks (AAAI 2019)

conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

convnet-burden

Memory consumption and FLOP count estimates for convnets

coot-videotext

COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning

couplenet

Code for training 'CoupleNet: Coupling Global Structure with Local Parts for Object Detection' in ICCV 2017

crest-release

CREST: Convolutional Residual Learning for Visual Tracking

This project is an implementation of the crowd counting model proposed in our CVPR 2017 paper - Switching Convolutional Neural Network(SCNN) for Crowd Counting. SCNN is an adaptation of the fully-convolutional neural network and uses an expert CNN that chooses the best crowd density CNN regressor for parts of the scene from a bag of regressors. This helps it tackle intra-scene crowd density variation and obtain SOTA results

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