Search Results for author: Marc Niethammer

Found 78 papers, 40 papers with code

Adversarial Data Augmentation via Deformation Statistics

no code implementations ECCV 2020 Sahin Olut, Zhengyang Shen, Zhenlin Xu, Samuel Gerber, Marc Niethammer

Data augmentation or semi-supervised approaches are commonly used to cope with limited labeled training data.

Data Augmentation

CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

1 code implementation10 Jun 2024 Peng Xia, Ze Chen, Juanxi Tian, Yangrui Gong, Ruibo Hou, Yue Xu, Zhenbang Wu, Zhiyuan Fan, Yiyang Zhou, Kangyu Zhu, Wenhao Zheng, Zhaoyang Wang, Xiao Wang, Xuchao Zhang, Chetan Bansal, Marc Niethammer, Junzhou Huang, Hongtu Zhu, Yun Li, Jimeng Sun, ZongYuan Ge, Gang Li, James Zou, Huaxiu Yao

Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare.

Fairness

Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts

1 code implementation31 Mar 2024 Qin Liu, Jaemin Cho, Mohit Bansal, Marc Niethammer

In light of this, we reintroduce this dense design into the generalist models, to facilitate the development of generalist models with high segmentation quality.

Image Segmentation Interactive Segmentation +2

Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos

no code implementations26 Mar 2024 Akshay Paruchuri, Samuel Ehrenstein, Shuxian Wang, Inbar Fried, Stephen M. Pizer, Marc Niethammer, Roni Sengupta

Monocular depth estimation in endoscopy videos can enable assistive and robotic surgery to obtain better coverage of the organ and detection of various health issues.

Monocular Depth Estimation Transfer Learning

A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness

1 code implementation18 Mar 2024 Boqi Chen, Junier Oliva, Marc Niethammer

Medical records often consist of different modalities, such as images, text, and tabular information.

NeuralOCT: Airway OCT Analysis via Neural Fields

no code implementations15 Mar 2024 Yining Jiao, Amy Oldenburg, Yinghan Xu, Srikamal Soundararajan, Carlton Zdanski, Julia Kimbell, Marc Niethammer

Our interest in this work is OCT in the context of airway abnormalities in infants and children where the high resolution of OCT and the fact that it is radiation-free is important.

3D Reconstruction

uniGradICON: A Foundation Model for Medical Image Registration

1 code implementation9 Mar 2024 Lin Tian, Hastings Greer, Roland Kwitt, Francois-Xavier Vialard, Raul San Jose Estepar, Sylvain Bouix, Richard Rushmore, Marc Niethammer

We therefore propose uniGradICON, a first step toward a foundation model for registration providing 1) great performance \emph{across} multiple datasets which is not feasible for current learning-based registration methods, 2) zero-shot capabilities for new registration tasks suitable for different acquisitions, anatomical regions, and modalities compared to the training dataset, and 3) a strong initialization for finetuning on out-of-distribution registration tasks.

Image Registration Medical Image Registration

Rethinking Interactive Image Segmentation with Low Latency High Quality and Diverse Prompts

1 code implementation CVPR 2024 Qin Liu, Jaemin Cho, Mohit Bansal, Marc Niethammer

In light of this we reintroduce this dense design into the generalist models to facilitate the development of generalist models with high segmentation quality.

Image Segmentation Interactive Segmentation +2

SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation

1 code implementation25 Nov 2023 Lin Tian, Zi Li, Fengze Liu, Xiaoyu Bai, Jia Ge, Le Lu, Marc Niethammer, Xianghua Ye, Ke Yan, Daikai Jin

In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration building on top of a Self-supervised Anatomical eMbedding (SAM) algorithm, which is capable of computing dense anatomical correspondences between two images at the voxel level.

Image Registration Medical Image Registration

$\texttt{NePhi}$: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration

no code implementations13 Sep 2023 Lin Tian, Hastings Greer, Raúl San José Estépar, Soumyadip Sengupta, Marc Niethammer

In contrast to the predominant voxel-based transformation fields used in learning-based registration approaches, NePhi represents deformations functionally, leading to great flexibility within the design space of memory consumption during training and inference, inference time, registration accuracy, as well as transformation regularity.

Diffeomorphic Medical Image Registration Image Registration

Self-supervised Landmark Learning with Deformation Reconstruction and Cross-subject Consistency Objectives

no code implementations9 Aug 2023 Chun-Hung Chao, Marc Niethammer

We argue that data with complicated deformations can not easily be modeled with point-based registration when only a limited number of points is used to extract influential landmark points.

Multimodal Understanding Through Correlation Maximization and Minimization

no code implementations4 May 2023 Yifeng Shi, Marc Niethammer

To answer 2), we propose novel scores that summarize the learned common and individual structures and visualize the score gradients with respect to the input, visually discerning what the different representations capture.

Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features

1 code implementation28 Apr 2023 Nurislam Tursynbek, Marc Niethammer

Inspired by recent findings that generative diffusion models learn semantically meaningful representations, we use them to discover the intrinsic hierarchical structure in biomedical 3D images using unsupervised segmentation.

Segmentation

MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities

no code implementations17 Mar 2023 Boqi Chen, Marc Niethammer

We use metric learning via multi-modal image retrieval, resulting in embeddings that can relate images of different modalities.

Image Generation Image Retrieval +2

NAISR: A 3D Neural Additive Model for Interpretable Shape Representation

1 code implementation16 Mar 2023 Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Marc Niethammer

However, given a set of 3D shapes with associated covariates there is at present no shape representation method which allows to precisely represent the shapes while capturing the individual dependencies on each covariate.

3D Shape Reconstruction Disentanglement +1

Exploring Cycle Consistency Learning in Interactive Volume Segmentation

1 code implementation11 Mar 2023 Qin Liu, Meng Zheng, Benjamin Planche, Zhongpai Gao, Terrence Chen, Marc Niethammer, Ziyan Wu

Given a medical volume, a user first segments a slice (or several slices) via the interaction module and then propagates the segmentation(s) to the remaining slices.

Segmentation

Harmonization Benchmarking Tool for Neuroimaging Datasets

1 code implementation15 Nov 2022 Tom Osika, Ebrahim Ebrahim, Martin Styner, Marc Niethammer, Thomas Sawyer, Andinet Enquobahrie

A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robust algorithms.

Benchmarking

Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language

no code implementations2 Oct 2022 Zhenlin Xu, Marc Niethammer, Colin Raffel

In hopes of enabling compositional generalization, various unsupervised learning algorithms have been proposed with inductive biases that aim to induce compositional structure in learned representations (e. g. disentangled representation and emergent language learning).

Disentanglement

Optimal Transport Features for Morphometric Population Analysis

1 code implementation11 Aug 2022 Samuel Gerber, Marc Niethammer, Ebrahim Ebrahim, Joseph Piven, Stephen R. Dager, Martin Styner, Stephen Aylward, Andinet Enquobahrie

We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease.

PseudoClick: Interactive Image Segmentation with Click Imitation

no code implementations12 Jul 2022 Qin Liu, Meng Zheng, Benjamin Planche, Srikrishna Karanam, Terrence Chen, Marc Niethammer, Ziyan Wu

The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i. e., by a minimal number of user clicks.

Image Segmentation Segmentation +1

Fluid registration between lung CT and stationary chest tomosynthesis images

1 code implementation6 Mar 2022 Lin Tian, Connor Puett, Peirong Liu, Zhengyang Shen, Stephen R. Aylward, Yueh Z. Lee, Marc Niethammer

We demonstrate our approach for the registration between CT and stationary chest tomosynthesis (sDCT) images and show how it naturally leads to an iterative image reconstruction approach.

Computed Tomography (CT) Image Reconstruction

On Measuring Excess Capacity in Neural Networks

1 code implementation16 Feb 2022 Florian Graf, Sebastian Zeng, Bastian Rieck, Marc Niethammer, Roland Kwitt

We study the excess capacity of deep networks in the context of supervised classification.

iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images

1 code implementation21 Dec 2021 Qin Liu, Zhenlin Xu, Yining Jiao, Marc Niethammer

We propose iSegFormer, a memory-efficient transformer that combines a Swin transformer with a lightweight multilayer perceptron (MLP) decoder.

Decoder Image Segmentation +3

The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

no code implementations13 Dec 2021 Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari, Michel Bilello, Benedikt Wiestler, Julian Schwarting, Evan Calabrese, Jeffrey Rudie, Syed Abidi, Mina Mousa, Javier Villanueva-Meyer, Brandon K. K. Fields, Florian Kofler, Russell Takeshi Shinohara, Juan Eugenio Iglesias, Tony C. W. Mok, Albert C. S. Chung, Marek Wodzinski, Artur Jurgas, Niccolo Marini, Manfredo Atzori, Henning Muller, Christoph Grobroehmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich, Luca Canalini, Jan Klein, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn, Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim, Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert, Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt, Kewei Yan, Yonghong Yan, Zhe Tang, Jianqiang Ma, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi, Nicholas J. Tustison, Brian B. Avants, Philip Cook, James C. Gee, Lin Tian, Hastings Greer, Marc Niethammer, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Stergios Christodoulidis, Theo Estiene, Maria Vakalopoulou, Nikos Paragios, Daniel S. Marcus, Christos Davatzikos, Aristeidis Sotiras, Bjoern Menze, Spyridon Bakas, Diana Waldmannstetter

Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.

Descriptive Image Registration +1

Deep Decomposition for Stochastic Normal-Abnormal Transport

no code implementations CVPR 2022 Peirong Liu, Yueh Lee, Stephen Aylward, Marc Niethammer

Extensive comparisons demonstrate that our model successfully distinguishes stroke lesions (abnormal) from normal brain regions, while reconstructing the underlying velocity and diffusion tensor fields.

Optical Flow Estimation Time Series +2

Accurate Point Cloud Registration with Robust Optimal Transport

2 code implementations NeurIPS 2021 Zhengyang Shen, Jean Feydy, Peirong Liu, Ariel Hernán Curiale, Ruben San Jose Estepar, Raul San Jose Estepar, Marc Niethammer

Finally, we showcase the performance of transport-enhanced registration models on a wide range of challenging tasks: rigid registration for partial shapes; scene flow estimation on the Kitti dataset; and nonparametric registration of lung vascular trees between inspiration and expiration.

Point Cloud Registration Scene Flow Estimation

ICON: Learning Regular Maps Through Inverse Consistency

2 code implementations ICCV 2021 Hastings Greer, Roland Kwitt, Francois-Xavier Vialard, Marc Niethammer

We explore if it is possible to obtain spatial regularity using an inverse consistency loss only and elucidate what explains map regularity in such a context.

Representation Learning Translation

Dissecting Supervised Contrastive Learning

1 code implementation17 Feb 2021 Florian Graf, Christoph D. Hofer, Marc Niethammer, Roland Kwitt

Minimizing cross-entropy over the softmax scores of a linear map composed with a high-capacity encoder is arguably the most popular choice for training neural networks on supervised learning tasks.

Contrastive Learning

Discovering Hidden Physics Behind Transport Dynamics

no code implementations CVPR 2021 Peirong Liu, Lin Tian, Yubo Zhang, Stephen R. Aylward, Yueh Z. Lee, Marc Niethammer

To help with identifiability, we develop an advection-diffusion simulator which allows pre-training of our model by supervised learning using the velocity and diffusion tensor fields.

Optical Flow Estimation Time Series +2

VoteNet++: Registration Refinement for Multi-Atlas Segmentation

no code implementations26 Oct 2020 Zhipeng Ding, Marc Niethammer

Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images.

Image Segmentation Segmentation +1

Perfusion Imaging: A Data Assimilation Approach

1 code implementation6 Sep 2020 Peirong Liu, Yueh Z. Lee, Stephen R. Aylward, Marc Niethammer

In this work we therefore propose a data-assimilation approach (PIANO) which estimates the velocity and diffusion fields of an advection-diffusion model that best explains the contrast dynamics.

Computed Tomography (CT)

The Fairness-Accuracy Pareto Front

no code implementations25 Aug 2020 Susan Wei, Marc Niethammer

Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms.

Decision Making Fairness

A Deep Network for Joint Registration and Reconstruction of Images with Pathologies

no code implementations17 Aug 2020 Xu Han, Zhengyang Shen, Zhenlin Xu, Spyridon Bakas, Hamed Akbari, Michel Bilello, Christos Davatzikos, Marc Niethammer

They are therefore not designed for the registration of images with strong pathologies for example in the context of brain tumors, and traumatic brain injuries.

Image Registration

Robust and Generalizable Visual Representation Learning via Random Convolutions

2 code implementations ICLR 2021 Zhenlin Xu, Deyi Liu, Junlin Yang, Colin Raffel, Marc Niethammer

In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation.

Data Augmentation Domain Generalization +1

A Shooting Formulation of Deep Learning

no code implementations NeurIPS 2020 François-Xavier Vialard, Roland Kwitt, Susan Wei, Marc Niethammer

Continuous-depth neural networks can be viewed as deep limits of discrete neural networks whose dynamics resemble a discretization of an ordinary differential equation (ODE).

Deep Goal-Oriented Clustering

no code implementations7 Jun 2020 Yifeng Shi, Christopher M. Bender, Junier B. Oliva, Marc Niethammer

Clustering and prediction are two primary tasks in the fields of unsupervised and supervised learning, respectively.

Clustering

Topologically Densified Distributions

1 code implementation ICML 2020 Christoph D. Hofer, Florian Graf, Marc Niethammer, Roland Kwitt

We study regularization in the context of small sample-size learning with over-parameterized neural networks.

VoteNet+ : An Improved Deep Learning Label Fusion Method for Multi-atlas Segmentation

no code implementations1 Nov 2019 Zhipeng Ding, Xu Han, Marc Niethammer

Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF.

The fairness-accuracy landscape of neural classifiers

no code implementations25 Sep 2019 Susan Wei, Marc Niethammer

That machine learning algorithms can demonstrate bias is well-documented by now.

Attribute Causal Inference +1

Deep Message Passing on Sets

no code implementations21 Sep 2019 Yifeng Shi, Junier Oliva, Marc Niethammer

DMPS not only connects learning on graphs with learning on sets via deep kernel learning, but it also bridges message passing on sets and traditional diffusion dynamics commonly used in denoising models.

Denoising Relational Reasoning

Deep Multi-View Learning via Task-Optimal CCA

1 code implementation17 Jul 2019 Heather D. Couture, Roland Kwitt, J. S. Marron, Melissa Troester, Charles M. Perou, Marc Niethammer

Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use of class labels.

MULTI-VIEW LEARNING

Connectivity-Optimized Representation Learning via Persistent Homology

1 code implementation21 Jun 2019 Christoph Hofer, Roland Kwitt, Mandar Dixit, Marc Niethammer

In particular, we control the connectivity of an autoencoder's latent space via a novel type of loss, operating on information from persistent homology.

Representation Learning

Graph Filtration Learning

1 code implementation ICML 2020 Christoph D. Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt

We propose an approach to learning with graph-structured data in the problem domain of graph classification.

General Classification Graph Classification

Metric Learning for Image Registration

1 code implementation CVPR 2019 Marc Niethammer, Roland Kwitt, Francois-Xavier Vialard

Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.

Deformable Medical Image Registration Diffeomorphic Medical Image Registration +2

VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation

no code implementations18 Apr 2019 Zhipeng Ding, Xu Han, Marc Niethammer

Experiments on 3D brain MRI data show that by selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct DL segmentation approach.

Image Segmentation Medical Image Segmentation +2

DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation

1 code implementation17 Apr 2019 Zhenlin Xu, Marc Niethammer

Specifically, in a one-shot-scenario (with only one manually labeled image) our approach increases Dice scores (%) over an unsupervised registration network by 2. 7 and 1. 8 on the knee and brain images respectively.

Data Augmentation Image Segmentation +3

Networks for Joint Affine and Non-parametric Image Registration

2 code implementations CVPR 2019 Zhengyang Shen, Xu Han, Zhenlin Xu, Marc Niethammer

In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model.

Image Registration Medical Image Registration

Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

no code implementations13 Jun 2018 Heather D. Couture, J. S. Marron, Charles M. Perou, Melissa A. Troester, Marc Niethammer

We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function.

Classification General Classification +2

Stochastic Block Models with Multiple Continuous Attributes

no code implementations7 Mar 2018 Natalie Stanley, Thomas Bonacci, Roland Kwitt, Marc Niethammer, Peter J. Mucha

While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes.

Attribute Collaborative Filtering +3

Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging

no code implementations19 Jul 2017 Emilie Gerardin, Gaël Chételat, Marie Chupin, Rémi Cuingnet, Béatrice Desgranges, Ho-Sung Kim, Marc Niethammer, Bruno Dubois, Stéphane Lehéricy, Line Garnero, Francis Eustache, Olivier Colliot

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features.

Classification General Classification +1

Deep Learning with Topological Signatures

4 code implementations NeurIPS 2017 Christoph Hofer, Roland Kwitt, Marc Niethammer, Andreas Uhl

Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems.

BIG-bench Machine Learning Topological Data Analysis

AGA: Attribute-Guided Augmentation

1 code implementation CVPR 2017 Mandar Dixit, Roland Kwitt, Marc Niethammer, Nuno Vasconcelos

We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner.

Attribute Data Augmentation +4

Compressing networks with super nodes

1 code implementation13 Jun 2017 Natalie Stanley, Roland Kwitt, Marc Niethammer, Peter J. Mucha

Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns.

Social and Information Networks Physics and Society

Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach

no code implementations31 Mar 2017 Xu Han, Xiao Yang, Stephen Aylward, Roland Kwitt, Marc Niethammer

Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies.

Image Reconstruction

Fast Predictive Multimodal Image Registration

no code implementations31 Mar 2017 Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer

We introduce a deep encoder-decoder architecture for image deformation prediction from multimodal images.

Decoder Image Registration

Probabilistic Diffeomorphic Registration: Representing Uncertainty

no code implementations12 Jan 2017 Demian Wassermann, Matt Toews, Marc Niethammer, William Wells III

The Bayesian posterior distribution over the deformations aligning a moving and a fixed image is approximated via a variational formulation.

Image Registration

AGA: Attribute Guided Augmentation

1 code implementation8 Dec 2016 Mandar Dixit, Roland Kwitt, Marc Niethammer, Nuno Vasconcelos

We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner.

Attribute Data Augmentation +4

Fast Predictive Image Registration

no code implementations8 Jul 2016 Xiao Yang, Roland Kwitt, Marc Niethammer

We present a method to predict image deformations based on patch-wise image appearance.

Decoder Image Registration

One-Shot Learning of Scene Locations via Feature Trajectory Transfer

no code implementations CVPR 2016 Roland Kwitt, Sebastian Hegenbart, Marc Niethammer

In particular, we leverage a recently introduced dataset with fine-grain annotations to estimate feature trajectories for a collection of transient attributes and then show how these trajectories can be transferred to new image representations.

Attribute One-Shot Learning +1

Statistical Topological Data Analysis - A Kernel Perspective

no code implementations NeurIPS 2015 Roland Kwitt, Stefan Huber, Marc Niethammer, Weili Lin, Ulrich Bauer

We consider the problem of statistical computations with persistence diagrams, a summary representation of topological features in data.

Topological Data Analysis Two-sample testing

Parametric Regression on the Grassmannian

no code implementations14 May 2015 Yi Hong, Nikhil Singh, Roland Kwitt, Nuno Vasconcelos, Marc Niethammer

We then specialize this idea to the Grassmann manifold and demonstrate that it yields a simple, extensible and easy-to-implement solution to the parametric regression problem.

Crowd Counting regression

Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models

no code implementations22 Jan 2015 Marc Niethammer, Kilian M. Pohl, Firdaus Janoos, William M. Wells III

A specific implementation of that model is the Chan-Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary.

Image Denoising Image Segmentation +2

Scene Parsing with Object Instances and Occlusion Ordering

no code implementations CVPR 2014 Joseph Tighe, Marc Niethammer, Svetlana Lazebnik

This work proposes a method to interpret a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships.

Object Scene Parsing +1

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