josephgesnouin Goto Github PK

3dmppe_posenet_release

Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image ", ICCV 2019

adaptivebinning

Adaptive-binning for evaluation of confidence calibration

adascan-public

Code for AdaScan: Adaptive Scan Pooling (CVPR 2017)

alphapose

Real-Time and Accurate Multi-Person Pose Estimation&Tracking System

app-sup

Supervised learning project

asymmetrical-bi-rnns-to-encode-pedestrian-trajectories

Code for the paper: Asymmetrical Bi-RNNs to encode pedestrian trajectories on trajnet++ dataset

avancement1a

bim2

casr

Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation

cbam-keras

CBAM implementation on Keras

cbam.pytorch

Non-official implement of Paper：CBAM: Convolutional Block Attention Module

cclst

cclust_package

cd-diagram

Critical difference diagram with Wilcoxon-Holm post-hoc analysis.

cite

Implementation for our paper "Conditional Image-Text Embedding Networks"

classificationts

Classification TS: COR / DTW / ARIMA / GMM

coclust

codeforces

Python solutions to CodeForces problems

convolution_variants

Reimplementing SOTA convolution variants with Tensorflow 2.0.

convolutional-block-attention-module

Unofficial implementation of Convolutional Block Attention Module (CBAM)

data-science--cheat-sheet

Cheat Sheets

dcgan-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

dd-net

A lightweight network for body/hand action recognition

deepdr-dae-with-lle

Apprentissage Profond pour la Réduction de Dimension

densenet

DenseNet implementation in Keras

detection-attributes-fields

A PyTorch implementation of "Detecting 32 Pedestrian Attributes for Autonomous Vehicles"

detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

detectron2

Detectron2 is FAIR's next-generation research platform for object detection and segmentation.

dg-sta

dl-visuals

Over 200 figures and diagrams of the most popular deep learning architectures and layers FREE TO USE in your blog posts, slides, presentations, or papers.