Object-aware Contrastive Learning for Debiased Scene Representation

Overview

Object-aware Contrastive Learning

Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo Mo*, Hyunwoo Kang*, Kihyuk Sohn, Chun-Liang Li, and Jinwoo Shin.

Installation

Install required libraries.

pip install -r requirements.txt

Download datasets in /data (e.g., /data/COCO).

Train models

Logs will be saved in logs/{dataset}_{model}_{arch}_b{global_batch_size} directory, where global_batch_size = num_nodes * gpus * batch_size (default batch size = 64 * 4 = 256).

Step 1. Train vanilla models

Train vanilla models (change dataset and ft_datasets as cub or in9).

python pretrain.py --dataset coco --model moco --arch resnet18\
    --ft_datasets coco --batch_size 64 --max_epochs 800

Step 2. Pre-compute CAM masks

Pre-compute bounding boxes for object-aware random crop.

python inference.py --mode save_box --model moco --arch resnet18\
    --ckpt_name coco_moco_r18_b256 --dataset coco\
    --expand_res 2 --cam_iters 10 --apply_crf\
    --save_path data/boxes/coco_cam-r18.txt

Pre-compute masks for background mixup.

python inference.py --mode save_mask --model moco --arch resnet18\
    --ckpt_name in9_moco_r18_256 --dataset in9\
    --expand_res 1 --cam_iters 1\
    --save_path data/masks/in9_cam-r18

Step 3. Re-train debiased models

Train contextual debiased model with object-aware random crop.

python pretrain.py --dataset coco-box-cam-r18 --model moco --arch resnet18\
     --ft_datasets coco --batch_size 64 --max_epochs 800

Train background debiased model with background mixup.

python pretrain.py --dataset in9-mask-cam-r18 --model moco_bgmix --arch resnet18\
    --ft_datasets in9 --batch_size 64 --max_epochs 800

Evaluate models

Linear evaluation

python inference.py --mode lineval --model moco --arch resnet18\
    --ckpt_name coco_moco_r18_b256 --dataset coco

Object localization

python inference.py --mode seg --model moco --arch resnet18\
    --ckpt_name cub200_moco_r18_b256 --dataset cub200\
    --expand_res 2 --cam_iters 10 --apply_crf

Detection & Segmentation (fine-tuning)

mv detection
python convert-pretrain-to-detectron2.py coco_moco_r50.pth coco_moco_r50.pkl
python train_net.py --config-file configs/coco_R_50_C4_2x_moco.yaml --num-gpus 8\
    MODEL.WEIGHTS weights/coco_moco_r18.pkl
This repository contains the source codes for the paper AtlasNet V2 - Learning Elementary Structures.

AtlasNet V2 - Learning Elementary Structures This work was build upon Thibault Groueix's AtlasNet and 3D-CODED projects. (you might want to have a loo

Théo Deprelle 123 Nov 11, 2022
CondNet: Conditional Classifier for Scene Segmentation

CondNet: Conditional Classifier for Scene Segmentation Introduction The fully convolutional network (FCN) has achieved tremendous success in dense vis

ycszen 31 Jul 22, 2022
Pytorch implementation of the paper: "SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Image Deraining"

SAPNet This repository contains the official Pytorch implementation of the paper: "SAPNet: Segmentation-Aware Progressive Network for Perceptual Contr

11 Oct 17, 2022
LaneAF: Robust Multi-Lane Detection with Affinity Fields

LaneAF: Robust Multi-Lane Detection with Affinity Fields This repository contains Pytorch code for training and testing LaneAF lane detection models i

155 Dec 17, 2022
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation This repository contains the Pytorch implementation of the proposed

Devavrat Tomar 19 Nov 10, 2022
Migration of Edge-based Distributed Federated Learning

FedFly: Towards Migration in Edge-based Distributed Federated Learning About the research Due to mobility, a device participating in Federated Learnin

qub-blesson 11 Nov 13, 2022
Scribble-Supervised LiDAR Semantic Segmentation, CVPR 2022 (ORAL)

Scribble-Supervised LiDAR Semantic Segmentation Dataset and code release for the paper Scribble-Supervised LiDAR Semantic Segmentation, CVPR 2022 (ORA

102 Dec 25, 2022
TipToiDog - Tip Toi Dog With Python

TipToiDog Was ist dieses Projekt? Meine 5-jährige Tochter spielt sehr gerne das

1 Feb 07, 2022
A CROSS-MODAL FUSION NETWORK BASED ON SELF-ATTENTION AND RESIDUAL STRUCTURE FOR MULTIMODAL EMOTION RECOGNITION

CFN-SR A CROSS-MODAL FUSION NETWORK BASED ON SELF-ATTENTION AND RESIDUAL STRUCTURE FOR MULTIMODAL EMOTION RECOGNITION The audio-video based multimodal

skeleton 15 Sep 26, 2022
Few-shot Neural Architecture Search

One-shot Neural Architecture Search uses a single supernet to approximate the performance each architecture. However, this performance estimation is super inaccurate because of co-adaption among oper

Yiyang Zhao 38 Oct 18, 2022
Multi-agent reinforcement learning algorithm and environment

Multi-agent reinforcement learning algorithm and environment [en/cn] Pytorch implements multi-agent reinforcement learning algorithms including IQL, Q

万鲲鹏 7 Sep 20, 2022
On the model-based stochastic value gradient for continuous reinforcement learning

On the model-based stochastic value gradient for continuous reinforcement learning This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, a

Facebook Research 46 Dec 15, 2022
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning (ECCV 2020 Oral and TRO)

Visual Interestingness Refer to the project description for more details. This code based on the following paper. Chen Wang, Yuheng Qiu, Wenshan Wang,

Chen Wang 36 Sep 08, 2022
Rethinking Portrait Matting with Privacy Preserving

Rethinking Portrait Matting with Privacy Preserving This is the official repository of the paper Rethinking Portrait Matting with Privacy Preserving.

184 Jan 03, 2023
A library for differentiable nonlinear optimization.

Theseus A library for differentiable nonlinear optimization built on PyTorch to support constructing various problems in robotics and vision as end-to

Meta Research 1.1k Dec 30, 2022
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches

BLEND is a mechanism that can efficiently find fuzzy seed matches between sequences to significantly improve the performance and accuracy while reducing the memory space usage of two important applic

SAFARI Research Group at ETH Zurich and Carnegie Mellon University 19 Dec 26, 2022
Azua - build AI algorithms to aid efficient decision-making with minimum data requirements.

Project Azua 0. Overview Many modern AI algorithms are known to be data-hungry, whereas human decision-making is much more efficient. The human can re

Microsoft 197 Jan 06, 2023
Code for "ATISS: Autoregressive Transformers for Indoor Scene Synthesis", NeurIPS 2021

ATISS: Autoregressive Transformers for Indoor Scene Synthesis This repository contains the code that accompanies our paper ATISS: Autoregressive Trans

138 Dec 22, 2022
The code repository for "PyCIL: A Python Toolbox for Class-Incremental Learning" in PyTorch.

PyCIL: A Python Toolbox for Class-Incremental Learning Introduction • Methods Reproduced • Reproduced Results • How To Use • License • Acknowledgement

Fu-Yun Wang 258 Dec 31, 2022
A big endian Gentoo port developed on a Pine64.org RockPro64

Gentoo-aarch64_be A big endian Gentoo port developed on a Pine64.org RockPro64 The endian wars are over... little endian won. As a result, it is incre

Rory Bolt 6 Dec 07, 2022