STEAL - Learning Semantic Boundaries from Noisy Annotations (CVPR 2019)

Overview

STEAL

This is the official inference code for:

Devil Is in the Edges: Learning Semantic Boundaries from Noisy Annotations

David Acuna, Amlan Kar, Sanja Fidler

CVPR 2019 [Paper] [Project Page]

STEAL DEMO

License

# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#  * Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.
#  * Redistributions in binary form must reproduce the above copyright
#    notice, this list of conditions and the following disclaimer in the
#    documentation and/or other materials provided with the distribution.
#  * Neither the name of NVIDIA CORPORATION nor the names of its
#    contributors may be used to endorse or promote products derived
#    from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Usage

Clone this repo
git clone https://github.com/nv-tlabs/STEAL
cd STEAL

Install dependencies

This code requires PyTorch 0.4 and python 3+. Please install dependencies by

pip install -r requirments.txt

Download pretrained models

Download the tar of the pretrained models from the Google Drive Folder, save it in 'checkpoints/', and run

cd checkpoints
tar -xvf checkpoints.tar.gz
cd ../

Inference (SBD)

python inference_sbd.py \
    --root_dir_val= ./data/sbd/data_aug/\
    --flist_val= ./data/sbd/data_aug/val_list.txt\
    --output_folder=./output/sbd/ \
    --ckpt=./checkpoints/sbd/model_checkpoint.pt\

Instructions and preprocessing scripts to download SBD and preprocess the dataset can be found here: https://github.com/Chrisding/sbd-preprocess

Inference (Cityscapes)

python inference_cityscapes.py \
    --root_dir_val=./data/cityscapes-preprocess/data_proc \
    --flist_val=./data_proc/val.txt \
    --output_folder=./output/cityscapes/ \
    --ckpt=./checkpoints/cityscapes/model_checkpoint.pt\

Instructions and preprocessing scripts for Cityscapes can be found here: https://github.com/Chrisding/cityscapes-preprocess

Test-NMS: An example of how to apply TEST-NMS using Piotr's Structured Forest matlab toolbox. can be found in utils/edges_nms.m. During training, we optimized for the same set of operations with r=2 (Check paper for more details)

Coarse-to-fine Demo

Checkout the ipython notebook that provides a simple walkthrough demonstrating how to run our model to refine coarsely annotated data.

Coarse to Fine

If you use this code, please cite:

@inproceedings{AcunaCVPR19STEAL,
title={Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations},
author={David Acuna and Amlan Kar and Sanja Fidler},
booktitle={CVPR},
year={2019}
}
Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Yihong Sun 12 Nov 15, 2022
Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation"

CoCosNet Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation" (CVPR 2020 oral). Update: 202

Lingbo Yang 38 Sep 22, 2021
Pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion"

MOSNet pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion" https://arxiv.org/abs/1904.08352 Dependency L

9 Nov 18, 2022
Hand tracking demo for DIY Smart Glasses with a remote computer doing the work

CameraStream This is a demonstration that streams the image from smartglasses to a pc, does the hand recognition on the remote pc and streams the proc

Teemu Laurila 20 Oct 13, 2022
(SIGIR2020) “Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback’’

Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback About This repository accompanies the real-world experiments conducted i

yuta-saito 19 Dec 01, 2022
Online-compatible Unsupervised Non-resonant Anomaly Detection Repository

Online-compatible Unsupervised Non-resonant Anomaly Detection Repository Repository containing all scripts used in the studies of Online-compatible Un

0 Nov 09, 2021
Notes taking website build with Docker + Django + React.

Notes website. Try it in browser! / But how to run? Description. This is monorepository with notes website. Website provides web interface for creatin

Kirill Zhosul 2 Jul 27, 2022
Unet network with mean teacher for altrasound image segmentation

Unet network with mean teacher for altrasound image segmentation

5 Nov 21, 2022
Official Implementation of VAT

Semantic correspondence Few-shot segmentation Cost Aggregation Is All You Need for Few-Shot Segmentation For more information, check out project [Proj

Hamacojr 114 Dec 27, 2022
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

[ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training The Unreasonable Effectiveness of

VITA 44 Dec 23, 2022
Implementation of FitVid video prediction model in JAX/Flax.

FitVid Video Prediction Model Implementation of FitVid video prediction model in JAX/Flax. If you find this code useful, please cite it in your paper:

Google Research 62 Nov 25, 2022
CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).

CCNet: Criss-Cross Attention for Semantic Segmentation Paper Links: Our most recent TPAMI version with improvements and extensions (Earlier ICCV versi

Zilong Huang 1.3k Dec 27, 2022
OCR-D wrapper for detectron2 based segmentation models

ocrd_detectron2 OCR-D wrapper for detectron2 based segmentation models Introduction Installation Usage OCR-D processor interface ocrd-detectron2-segm

Robert Sachunsky 13 Dec 06, 2022
Official implementation of the ICCV 2021 paper "Conditional DETR for Fast Training Convergence".

The DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergen

281 Dec 30, 2022
A Python Library for Graph Outlier Detection (Anomaly Detection)

PyGOD is a Python library for graph outlier detection (anomaly detection). This exciting yet challenging field has many key applications, e.g., detect

PyGOD Team 757 Jan 04, 2023
transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛

transfer_adv CVPR-2021 AIC-VI: unrestricted Adversarial Attacks on ImageNet CVPR2021 安全AI挑战者计划第六期赛道2:ImageNet无限制对抗攻击 介绍 : 深度神经网络已经在各种视觉识别问题上取得了最先进的性能。

25 Dec 08, 2022
Preprocessed Datasets for our Multimodal NER paper

Unified Multimodal Transformer (UMT) for Multimodal Named Entity Recognition (MNER) Two MNER Datasets and Codes for our ACL'2020 paper: Improving Mult

76 Dec 21, 2022
Code for the paper: On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations

Non-Parametric Prior Actor-Critic (N-PPAC) This repository contains the code for On Pathologies in KL-Regularized Reinforcement Learning from Expert D

Cong Lu 5 May 13, 2022
Multi-View Radar Semantic Segmentation

Multi-View Radar Semantic Segmentation Paper Multi-View Radar Semantic Segmentation, ICCV 2021. Arthur Ouaknine, Alasdair Newson, Patrick Pérez, Flore

valeo.ai 37 Oct 25, 2022
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer

BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer Project Page | Paper | Video State-of-the-art image-to-image translatio

47 Dec 06, 2022