jdc08161063/adaptive_affinity_fields

Adaptive Affinity Fields for Semantic Segmentation

Adaptive Affinity Fields for Semantic Segmentation

By Tsung-Wei Ke*, Jyh-Jing Hwang*, Ziwei Liu, and Stella X. Yu (* equal contribution)

Semantic segmentation has made much progress with increasingly powerful pixel-wise classifiers and incorporating structural priors via Conditional Random Fields (CRF) or Generative Adversarial Networks (GAN). We propose a simpler alternative that learns to verify the spatial structure of segmentation during training only. Unlike existing approaches that enforce semantic labels on individual pixels and match labels between neighbouring pixels, we propose the concept of Adaptive Affinity Fields (AAF) to capture and match the semantic relations between neighbouring pixels in the label space. We use adversarial learning to select the optimal affinity field size for each semantic category. It is formulated as a minimax problem, optimizing our segmentation neural network in a best worst-case learning scenario. AAF is versatile for representing structures as a collection of pixel-centric relations, easier to train than GAN and more efficient than CRF without run-time inference. Our extensive evaluations on PASCAL VOC 2012, Cityscapes, and GTA5 datasets demonstrate its above-par segmentation performance and robust generalization across domains.

AAF is published in ECCV 2018, see our paper for more details.

Note: SyncBN is releasing soon. Before that, you might not be able to reproduce our results on Cityscapes.

Prerequisites

1. Linux
2. Python2.7 or Python3 (>=3.5)
3. Cuda 8.0 and Cudnn 6

Required Python Packages

1. tensorflow 1.4 (for versions >= 1.6 might cause OOM error)
2. numpy
3. scipy
4. tqdm
5. PIL
6. opencv

Data Preparation

• PASCAL VOC 2012
• Cityscapes

ImageNet Pre-Trained Models

Download ResNet101.v1 from Tensorflow-Slim.

Training

• Baseline Models:

python pyscripts/train/train.py

• Affinity

python pyscripts/train/train_aff.py

• AAF

python pyscripts/train/train_aaf.py

Inference

• Single-Scale Input only

python pyscripts/inference/inference.py

• Multi-Scale Inputs and Left-Right Flipping (opencv is required)

python pyscripts/inference/inference_msc.py

Benchmarking

• mIoU

python pyscripts/benchmark/benchmark_by_mIoU.py

See our bash script examples for the corresponding input arguments.

Citation

If you find this code useful for your research, please consider citing our paper Adaptive Affinity Fields for Semantic Segmentation.

@inproceedings{aaf2018,
 author = {Ke, Tsung-Wei and Hwang, Jyh-Jing and Liu, Ziwei and Yu, Stella X.},
 title = {Adaptive Affinity Fields for Semantic Segmentation},
 booktitle = {European Conference on Computer Vision (ECCV)},
 month = {September},
 year = {2018} 
}

License

AAF is released under the MIT License (refer to the LICENSE file for details).