[ICCV-2021] An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation

Overview

An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation (ICCV 2021)

Introduction

This is an official pytorch implementation of An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation. [ICCV 2021] PDF

Abstract

Most semi-supervised learning models are consistency-based, which leverage unlabeled images by maximizing the similarity between different augmentations of an image. But when we apply them to human pose estimation that has extremely imbalanced class distribution, they often collapse and predict every pixel in unlabeled images as background. We find this is because the decision boundary passes the high-density areas of the minor class so more and more pixels are gradually mis-classified as background.

In this work, we present a surprisingly simple approach to drive the model. For each image, it composes a pair of easy-hard augmentations and uses the more accurate predictions on the easy image to teach the network to learn pose information of the hard one. The accuracy superiority of teaching signals allows the network to be “monotonically” improved which effectively avoids collapsing. We apply our method to the state-of-the-art pose estimators and it further improves their performance on three public datasets.

Main Results

1. Semi-Supervised Setting

Results on COCO Val2017

Method Augmentation 1K Labels 5K Labels 10K Labels
Supervised Affine 31.5 46.4 51.1
PoseCons (Single) Affine 38.5 50.5 55.4
PoseCons (Single) Affine + Joint Cutout 42.1 52.3 57.3
PoseDual (Dual) Affine 41.5 54.8 58.7
PoseDual (Dual) Affine + RandAug 43.7 55.4 59.3
PoseDual (Dual) Affine + Joint Cutout 44.6 55.6 59.6

We use COCO Subset (1K, 5K and 10K) and TRAIN as labeled and unlabeled datasets, respectively

Note:

  • The Ground Truth person boxes is used
  • No flipping test is used.

2. Full labels Setting

Results on COCO Val2017

Method Network AP AP.5 AR
Supervised ResNet50 70.9 91.4 74.2
PoseDual ResNet50 73.9 (↑3.0) 92.5 77.0
Supervised HRNetW48 77.2 93.5 79.9
PoseDual HRNetW48 79.2 (↑2.0) 94.6 81.7

We use COCO TRAIN and WILD as labeled and unlabeled datasets, respectively

Pretrained Models

Download Links Google Drive

Environment

The code is developed using python 3.7 on Ubuntu 16.04. NVIDIA GPUs are needed.

Quick start

Installation

  1. Install pytorch >= v1.2.0 following official instruction.

  2. Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}.

  3. Install dependencies:

    pip install -r requirements.txt
    
  4. Make libs:

    cd ${POSE_ROOT}/lib
    make
    
  5. Init output(training model output directory)::

     mkdir output 
     mkdir log
    
  6. Download pytorch imagenet pretrained models from Google Drive. The PoseDual (ResNet18) should load resnet18_5c_gluon_posedual as pretrained for training,

  7. Download our pretrained models from Google Drive

    ${POSE_ROOT}
     `-- models
         `-- pytorch
             |-- imagenet
             |   |-- resnet18_5c_f3_posedual.pth
             |   |-- resnet18-5c106cde.pth
             |   |-- resnet50-19c8e357.pth
             |   |-- resnet101-5d3b4d8f.pth
             |   |-- resnet152-b121ed2d.pth
             |   |-- ......
             |-- pose_dual
                 |-- COCO_subset
                 |   |-- COCO1K_PoseDual.pth.tar
                 |   |-- COCO5K_PoseDual.pth.tar
                 |   |-- COCO10K_PoseDual.pth.tar
                 |   |-- ......
                 |-- COCO_COCOwild
                 |-- ......
    

Data preparation

For COCO and MPII dataset, Please refer to Simple Baseline to prepare them.
Download Person Detection Boxes and Images for COCO WILD (unlabeled) set. The structure looks like this:

${POSE_ROOT}
|-- data
`-- |-- coco
    `-- |-- annotations
        |   |-- person_keypoints_train2017.json
        |   |-- person_keypoints_val2017.json
        |   `__ image_info_unlabeled2017.json
        |-- person_detection_results
        |   |-- COCO_val2017_detections_AP_H_56_person.json
        |   |-- COCO_test-dev2017_detections_AP_H_609_person.json
        |   `-- COCO_unlabeled2017_detections_person_faster_rcnn.json
        `-- images
            |-- train2017
            |   |-- 000000000009.jpg
            |   |-- 000000000025.jpg
            |   |-- ... 
            `-- val2017
                |-- 000000000139.jpg
                |-- 000000000285.jpg
                |-- ... 

For AIC data, please download from AI Challenger 2017, 2017 Train/Val is needed for keypoints training and validation. Please download the annotation files from AIC Annotations. The structure looks like this:

${POSE_ROOT}
|-- data
`-- |-- ai_challenger
    `-- |-- train
        |   |-- images
        |   `-- keypoint_train_annotation.json
        `-- validation
            |-- images
            |   |-- 0a00c0b5493774b3de2cf439c84702dd839af9a2.jpg
            |   |-- 0a0c466577b9d87e0a0ed84fc8f95ccc1197f4b0.jpg
            |   `-- ...
            |-- gt_valid.mat
            `-- keypoint_validation_annotation.json

Run

Training

1. Training Dual Networks (PoseDual) on COCO 1K labels

python pose_estimation/train.py \
    --cfg experiments/mix_coco_coco/res18/256x192_COCO1K_PoseDual.yaml

2. Training Dual Networks on COCO 1K labels with Joint Cutout

python pose_estimation/train.py \
    --cfg experiments/mix_coco_coco/res18/256x192_COCO1K_PoseDual_JointCutout.yaml

3.Training Dual Networks on COCO 1K labels with Distributed Data Parallel

python -m torch.distributed.launch --nproc_per_node=4  pose_estimation/train.py \
    --distributed --cfg experiments/mix_coco_coco/res18/256x192_COCO1K_PoseDual.yaml

4. Training Single Networks (PoseCons) on COCO 1K labels

python pose_estimation/train.py \
    --cfg experiments/mix_coco_coco/res18/256x192_COCO1K_PoseCons.yaml

5. Training Dual Networks (PoseDual) with ResNet50 on COCO TRAIN + WILD

python pose_estimation/train.py \
    --cfg experiments/mix_coco_coco/res50/256x192_COCO_COCOunlabel_PoseDual_JointCut.yaml

Testing

6. Testing Dual Networks (PoseDual+COCO1K) on COCO VAL

python pose_estimation/valid.py \
    --cfg experiments/mix_coco_coco/res18/256x192_COCO1K_PoseDual.yaml

Citation

If you use our code or models in your research, please cite with:

@inproceedings{semipose,
  title={An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation},
  author={Xie, Rongchang and Wang, Chunyu and Zeng, Wenjun and Wang, Yizhou},
  booktitle={ICCV},
  year={2021}
}

Acknowledgement

The code is mainly based on Simple Baseline and HRNet. Some code comes from DarkPose. Thanks for their works.

Owner
rongchangxie
Graduate student of Peking university
rongchangxie
Code for the paper titled "Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks" (NeurIPS 2021 Spotlight).

Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks This repository contains the code and pre-trained

Hassan Dbouk 7 Dec 05, 2022
Python PID Tuner - Makes a model of the System from a Process Reaction Curve and calculates PID Gains

PythonPID_Tuner_SOPDT Step 1: Takes a Process Reaction Curve in csv format - assumes data at 100ms interval (column names CV and PV) Step 2: Makes a r

1 Jan 18, 2022
Combinatorially Hard Games where the levels are procedurally generated

puzzlegen Implementation of two procedurally simulated environments with gym interfaces. IceSlider: the agent needs to reach and stop on the pink squa

Autonomous Learning Group 3 Jun 26, 2022
Code and data form the paper BERT Got a Date: Introducing Transformers to Temporal Tagging

BERT Got a Date: Introducing Transformers to Temporal Tagging Satya Almasian*, Dennis Aumiller*, and Michael Gertz Heidelberg University Contact us vi

54 Dec 04, 2022
TensorFlow 2 implementation of the Yahoo Open-NSFW model

TensorFlow 2 implementation of the Yahoo Open-NSFW model

Bosco Yung 101 Jan 01, 2023
CS50's Introduction to Artificial Intelligence Test Scripts

CS50's Introduction to Artificial Intelligence Test Scripts 🤷‍♂️ What's this? 🤷‍♀️ This repository contains Python scripts to automate tests for mos

Jet Kan 2 Dec 28, 2022
prior-based-losses-for-medical-image-segmentation

Repository for papers: Benchmark: Effect of Prior-based Losses on Segmentation Performance: A Benchmark Midl: A Surprisingly Effective Perimeter-based

Rosana EL JURDI 9 Sep 07, 2022
Deploy optimized transformer based models on Nvidia Triton server

🤗 Hugging Face Transformer submillisecond inference 🤯 and deployment on Nvidia Triton server Yes, you can perfom inference with transformer based mo

Lefebvre Sarrut Services 1.2k Jan 05, 2023
Learning from Synthetic Humans, CVPR 2017

Learning from Synthetic Humans (SURREAL) Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev and Cordelia Schmid,

Gul Varol 538 Dec 18, 2022
Code in PyTorch for the convex combination linear IAF and the Householder Flow, J.M. Tomczak & M. Welling

VAE with Volume-Preserving Flows This is a PyTorch implementation of two volume-preserving flows as described in the following papers: Tomczak, J. M.,

Jakub Tomczak 87 Dec 26, 2022
🤖 A Python library for learning and evaluating knowledge graph embeddings

PyKEEN PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-m

PyKEEN 1.1k Jan 09, 2023
NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring

NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring Uncensored version of the following image can be found at https://i.

notAI.tech 1.1k Dec 29, 2022
These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

Few-shot-NLEs These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations". You can find the smal

Yordan Yordanov 0 Oct 21, 2022
alfred-py: A deep learning utility library for **human**

Alfred Alfred is command line tool for deep-learning usage. if you want split an video into image frames or combine frames into a single video, then a

JinTian 800 Jan 03, 2023
(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

IsoTree Fast and multi-threaded implementation of Extended Isolation Forest, Fair-Cut Forest, SCiForest (a.k.a. Split-Criterion iForest), and regular

141 Dec 29, 2022
Neural Surface Maps

Neural Surface Maps Official implementation of Neural Surface Maps - Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra [Paper] [Project Page]

Luca Morreale 49 Dec 13, 2022
TensorFlow (Python) implementation of DeepTCN model for multivariate time series forecasting.

DeepTCN TensorFlow TensorFlow (Python) implementation of multivariate time series forecasting model introduced in Chen, Y., Kang, Y., Chen, Y., & Wang

Flavia Giammarino 21 Dec 19, 2022
Bolt Online Learning Toolbox

Bolt Online Learning Toolbox Bolt features discriminative learning of linear predictors (e.g. SVM or Logistic Regression) using fast online learning a

Peter Prettenhofer 87 Dec 12, 2022
Air Pollution Prediction System using Linear Regression and ANN

AirPollution Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living Publication Link:

Dr Sharnil Pandya, Associate Professor, Symbiosis International University 19 Feb 07, 2022
A multi-mode modulator for multi-domain few-shot classification (ICCV)

A multi-mode modulator for multi-domain few-shot classification (ICCV)

Yanbin Liu 8 Apr 28, 2022