Adam Kortylewski

research

∙ 08/19/2023

3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation

Regression-based methods for 3D human pose estimation directly predict t...

0 Yi Zhang, et al. ∙

research

∙ 06/13/2023

Adding 3D Geometry Control to Diffusion Models

Diffusion models have emerged as a powerful method of generative modelin...

0 Wufei Ma, et al. ∙

research

∙ 06/01/2023

Intriguing Properties of Text-guided Diffusion Models

Text-guided diffusion models (TDMs) are widely applied but can fail unex...

0 Qihao Liu, et al. ∙

research

∙ 06/01/2023

AvatarStudio: Text-driven Editing of 3D Dynamic Human Head Avatars

Capturing and editing full head performances enables the creation of vir...

0 Mohit Mendiratta, et al. ∙

research

∙ 05/31/2023

Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis

Human vision demonstrates higher robustness than current AI algorithms u...

0 Angtian Wang, et al. ∙

research

∙ 05/25/2023

Robust Category-Level 3D Pose Estimation from Synthetic Data

Obtaining accurate 3D object poses is vital for numerous computer vision...

0 Jiahao Yang, et al. ∙

research

∙ 05/24/2023

Robust 3D-aware Object Classification via Discriminative Render-and-Compare

In real-world applications, it is essential to jointly estimate the 3D o...

0 Artur Jesslen, et al. ∙

research

∙ 05/05/2023

General Neural Gauge Fields

The recent advance of neural fields, such as neural radiance fields, has...

0 Fangneng Zhan, et al. ∙

research

∙ 04/17/2023

OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images

Enhancing the robustness of vision algorithms in real-world scenarios is...

0 Bingchen Zhao, et al. ∙

research

∙ 03/13/2023

PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation

Human pose and shape (HPS) estimation methods achieve remarkable results...

0 Qihao Liu, et al. ∙

research

∙ 01/12/2023

Scene-Aware 3D Multi-Human Motion Capture from a Single Camera

In this work, we consider the problem of estimating the 3D position of m...

0 Diogo Luvizon, et al. ∙

research

∙ 12/01/2022

Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning

Visual Question Answering (VQA) models often perform poorly on out-of-di...

0 Zhuowan Li, et al. ∙

research

∙ 10/27/2022

State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

3D reconstruction of deformable (or non-rigid) scenes from a set of mono...

0 Edith Tretschk, et al. ∙

research

∙ 10/04/2022

HandFlow: Quantifying View-Dependent 3D Ambiguity in Two-Hand Reconstruction with Normalizing Flow

Reconstructing two-hand interactions from a single image is a challengin...

19 Jiayi Wang, et al. ∙

research

∙ 09/12/2022

Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features

We consider the problem of category-level 6D pose estimation from a sing...

6 Wufei Ma, et al. ∙

research

∙ 05/30/2022

VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis

Differentiable rendering allows the application of computer graphics on ...

4 Angtian Wang, et al. ∙

research

∙ 04/05/2022

SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering

While Visual Question Answering (VQA) has progressed rapidly, previous w...

11 Vipul Gupta, et al. ∙

research

∙ 12/02/2021

PartImageNet: A Large, High-Quality Dataset of Parts

A part-based object understanding facilitates efficient compositional le...

18 Ju He, et al. ∙

research

∙ 11/29/2021

ROBIN : A Benchmark for Robustness to Individual Nuisances in Real-World Out-of-Distribution Shifts

Enhancing the robustness in real-world scenarios has been proven very ch...

4 Bingchen Zhao, et al. ∙

research

∙ 10/27/2021

Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose

We study the problem of learning to estimate the 3D object pose from a f...

0 Angtian Wang, et al. ∙

research

∙ 10/26/2021

A Light-weight Interpretable CompositionalNetwork for Nuclei Detection and Weakly-supervised Segmentation

The field of computational pathology has witnessed great advancements si...

0 Yixiao Zhang, et al. ∙

research

∙ 07/12/2021

Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms

Self-Attention has become prevalent in computer vision models. Inspired ...

6 Chenglin Yang, et al. ∙

research

∙ 06/17/2021

To fit or not to fit: Model-based Face Reconstruction and Occlusion Segmentation from Weak Supervision

3D face reconstruction from a single image is challenging due to its ill...

0 Chunlu Li, et al. ∙

research

∙ 06/08/2021

Simulated Adversarial Testing of Face Recognition Models

Most machine learning models are validated and tested on fixed datasets....

0 Nataniel Ruiz, et al. ∙

research

∙ 06/01/2021

Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning

Semi-Supervised Learning (SSL) has shown its strong ability in utilizing...

11 Ju He, et al. ∙

research

∙ 03/25/2021

CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models

Part segmentations provide a rich and detailed part-level description of...

10 Qing Liu, et al. ∙

research

∙ 03/14/2021

TransFG: A Transformer Architecture for Fine-grained Recognition

Fine-grained visual classification (FGVC) which aims at recognizing obje...

12 Ju He, et al. ∙

research

∙ 02/22/2021

Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping

Catastrophic forgetting in neural networks is a significant problem for ...

0 Prakhar Kaushik, et al. ∙

research

∙ 01/29/2021

NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation

3D pose estimation is a challenging but important task in computer visio...

14 Angtian Wang, et al. ∙

research

∙ 01/28/2021

COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification

Few-shot image classification consists of two consecutive learning proce...

0 Ju He, et al. ∙

research

∙ 12/03/2020

Robust Instance Segmentation through Reasoning about Multi-Object Occlusion

Analyzing complex scenes with Deep Neural Networks is a challenging task...

9 Xiaoding Yuan, et al. ∙

research

∙ 12/01/2020

Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks

Patch-based adversarial attacks introduce a perceptible but localized ch...

0 Christian Cosgrove, et al. ∙

research

∙ 12/01/2020

Unsupervised Part Discovery via Feature Alignment

Understanding objects in terms of their individual parts is important, b...

7 Mengqi Guo, et al. ∙

research

∙ 10/25/2020

Weakly-Supervised Amodal Instance Segmentation with Compositional Priors

Amodal segmentation in biological vision refers to the perception of the...

0 Yihong Sun, et al. ∙

research

∙ 09/29/2020

CoKe: Localized Contrastive Learning for Robust Keypoint Detection

Today's most popular approaches to keypoint detection learn a holistic r...

8 Yutong Bai, et al. ∙

research

∙ 06/28/2020

Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion

Computer vision systems in real-world applications need to be robust to ...

0 Adam Kortylewski, et al. ∙

research

∙ 05/24/2020

Robust Object Detection under Occlusion with Context-Aware CompositionalNets

Detecting partially occluded objects is a difficult task. Our experiment...

16 Angtian Wang, et al. ∙

research

∙ 04/12/2020

PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning

Patch-based attacks introduce a perceptible but localized change to the ...

6 Chenglin Yang, et al. ∙

research

∙ 03/10/2020

Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion

Recent work has shown that deep convolutional neural networks (DCNNs) do...

0 Adam Kortylewski, et al. ∙

research

∙ 11/18/2019

Localizing Occluders with Compositional Convolutional Networks

Compositional convolutional networks are generative compositional models...

0 Adam Kortylewski, et al. ∙

research

∙ 09/09/2019

TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion

Despite deep convolutional neural networks' great success in object clas...

13 Mingqing Xiao, et al. ∙

research

∙ 09/03/2019

3D Morphable Face Models – Past, Present and Future

In this paper, we provide a detailed survey of 3D Morphable Face Models ...

0 Bernhard Egger, et al. ∙

research

∙ 05/28/2019

Compositional Convolutional Networks For Robust Object Classification under Occlusion

Deep convolutional neural networks (DCNNs) are powerful models that yiel...

0 Adam Kortylewski, et al. ∙

research

∙ 03/21/2019

SkelNetOn 2019 Dataset and Challenge on Deep Learning for Geometric Shape Understanding

We present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shap...

1 Ilke Demir, et al. ∙

research

∙ 11/19/2018

Priming Deep Neural Networks with Synthetic Faces for Enhanced Performance

Today's most successful facial image analysis systems are based on deep ...

0 Adam Kortylewski, et al. ∙

research

∙ 11/19/2018

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

Computer vision tasks are difficult because of the large variability in ...

0 Adam Kortylewski, et al. ∙

research

∙ 02/16/2018

Training Deep Face Recognition Systems with Synthetic Data

Recent advances in deep learning have significantly increased the perfor...

0 Adam Kortylewski, et al. ∙

research

∙ 12/05/2017

Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

It is unknown what kind of biases modern in the wild face datasets have ...

0 Adam Kortylewski, et al. ∙

research

∙ 01/22/2017

Greedy Compositional Clustering for Unsupervised Learning of Hierarchical Compositional Models

This paper proposes to integrate a feature pursuit learning process into...

0 Adam Kortylewski, et al. ∙

Adam Kortylewski

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