This repository contains the official implementation for the paper "Unsupervised Segmentation in Real-World Images via Spelke Object Inference" (ECCV 2022 Oral).

Paper: https://arxiv.org/abs/2205.08515
Project website: https://neuroailab.github.io/eisen/
Authors: Honglin Chen, Rahul Venkatesh, Yoni Friedman, Jiajun Wu, Joshua B. Tenenbaum, Daniel L. K. Yamins, Daniel M. Bear from Stanford University and MIT.

Dependency:

• Pytorch 1.11
• CUDA 11.3
• detectron2
• cc_torch

We recommend installation using Conda. Please make sure you have NVIDIA drivers supporting CUDA 11.3, or modify the version in `environment.yml.

conda env create -f environment.yml
conda activate eisen
pip install 'git+https://github.com/zsef123/Connected_components_PyTorch.git'
pip install 'git+https://github.com/facebookresearch/detectron2.git@5aeb252b194b93dc2879b4ac34bc51a31b5aee13'

To train the model on the ThreeWorld Playroom dataset (30G), please download the dataset and unzip it using the following command:

mkdir datasets
cd datasets
wget https://www.dropbox.com/s/yqw71eir9sjq4k4/Playroom.zip
unzip Playroom.zip

You can run the following command to precompute optical flow, which gives approximately 2x speed-up in training time.

CUDA_VISIBLE_DEVICES=0 python main.py --dataset playroom --batch_size 8 --precompute_flow

Alternative, precomputed optical flow (71G) can be downloaded directly:

cd datasets/Playroom
wget https://www.dropbox.com/s/5k9x5pahusdd0zk/Playroom_flows.zip
unzip Playroom_flows.zip

The training script can be run using the command below. By default, it assumes optical flows have been precomputed. Otherwise, you can simply add the flag --compute-flow to compute optical flows during training.

python main.py --name eisen --dataset playroom --batch_size 8

You can evaluate a training model using the command below. The checkpoints will be stored under ./checkpoints/.

CUDA_VISIBLE_DEVICES=0 python main.py --dataset playroom --batch_size 1 --eval_only --ckpt /path/to/checkpoint

• Support for bootstrapped training (coming soon)
• Support for training on Bridge dataset (coming soon)

@InProceedings{chen2022unsupervised,
author = {Chen, Honglin and Venkatesh, Rahul and Friedman, Yoni and Wu, Jiajun and Tenenbaum, Joshua B and Yamins, Daniel LK and Bear, Daniel M},
title = {Unsupervised Segmentation in Real-World Images via Spelke Object Inference},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022}
}

Our framework uses RAFT for computing optical flow from videos. Code related to connected component algorithm is adapted from Connected_components_Pytorch. Some code related to synchronized batch normalization is adpated from sync_batchnorm.