Search Results for author: Adam Kortylewski

Found 49 papers, 26 papers with code

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

no code implementations1 Jun 2023 Mohit Mendiratta, Xingang Pan, Mohamed Elgharib, Kartik Teotia, Mallikarjun B R, Ayush Tewari, Vladislav Golyanik, Adam Kortylewski, Christian Theobalt

Our method edits the full head in a canonical space, and then propagates these edits to remaining time steps via a pretrained deformation network.

Intriguing Properties of Text-guided Diffusion Models

1 code implementation1 Jun 2023 Qihao Liu, Adam Kortylewski, Yutong Bai, Song Bai, Alan Yuille

(2) We find samples in the latent space (which are not outliers) that lead to distorted images independent of the text prompt, suggesting that parts of the latent space are not well-structured.

Adversarial Attack Image Generation

Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis

no code implementations31 May 2023 Angtian Wang, Wufei Ma, Alan Yuille, Adam Kortylewski

Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios.

Robust Category-Level 3D Pose Estimation from Synthetic Data

no code implementations25 May 2023 Jiahao Yang, Wufei Ma, Angtian Wang, Xiaoding Yuan, Alan Yuille, Adam Kortylewski

In this work, we aim to narrow the performance gap between models trained on synthetic data and few real images and fully supervised models trained on large-scale data.

3D Pose Estimation 3D Reconstruction +4

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

no code implementations24 May 2023 Artur Jesslen, Guofeng Zhang, Angtian Wang, Alan Yuille, Adam Kortylewski

Using differentiable rendering, we estimate the 3D object pose by minimizing the reconstruction error between the mesh and the feature representation of the target image.

Classification Image Classification +1

General Neural Gauge Fields

no code implementations5 May 2023 Fangneng Zhan, Lingjie Liu, Adam Kortylewski, Christian Theobalt

To circumvent the high computation cost in gauge learning with regularization, we directly derive an information-invariant gauge transformation which allows to preserve scene information inherently and yield superior performance.

Representation Learning

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

1 code implementation CVPR 2023 Qihao Liu, Adam Kortylewski, Alan Yuille

We introduce a learning-based testing method, termed PoseExaminer, that automatically diagnoses HPS algorithms by searching over the parameter space of human pose images to find the failure modes.

Multi-agent Reinforcement Learning

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

1 code implementation12 Jan 2023 Diogo Luvizon, Marc Habermann, Vladislav Golyanik, Adam Kortylewski, Christian Theobalt

In this work, we consider the problem of estimating the 3D position of multiple humans in a scene as well as their body shape and articulation from a single RGB video recorded with a static camera.

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

no code implementations27 Oct 2022 Edith Tretschk, Navami Kairanda, Mallikarjun B R, Rishabh Dabral, Adam Kortylewski, Bernhard Egger, Marc Habermann, Pascal Fua, Christian Theobalt, Vladislav Golyanik

3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics.

3D Reconstruction

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

no code implementations4 Oct 2022 Jiayi Wang, Diogo Luvizon, Franziska Mueller, Florian Bernard, Adam Kortylewski, Dan Casas, Christian Theobalt

Through this, we demonstrate the quality of our probabilistic reconstruction and show that explicit ambiguity modeling is better-suited for this challenging problem.

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

1 code implementation30 May 2022 Angtian Wang, Peng Wang, Jian Sun, Adam Kortylewski, Alan Yuille

The Gaussian reconstruction kernels have been proposed by Westover (1990) and studied by the computer graphics community back in the 90s, which gives an alternative representation of object 3D geometry from meshes and point clouds.

Pose Estimation

Multimodal Image Synthesis and Editing: A Survey

1 code implementation27 Dec 2021 Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Lingjie Liu, Adam Kortylewski, Christian Theobalt, Eric Xing

With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years.

Image Generation

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

no code implementations29 Nov 2021 Bingchen Zhao, Shaozuo Yu, Wufei Ma, Mingxin Yu, Shenxiao Mei, Angtian Wang, Ju He, Alan Yuille, Adam Kortylewski

One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors.

3D Pose Estimation Benchmarking +4

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

1 code implementation NeurIPS 2021 Angtian Wang, Shenxiao Mei, Alan Yuille, Adam Kortylewski

The model is initialized from a few labelled images and is subsequently used to synthesize feature representations of unseen 3D views.

3D Pose Estimation Few-Shot Learning

Simulated Adversarial Testing of Face Recognition Models

no code implementations CVPR 2022 Nataniel Ruiz, Adam Kortylewski, Weichao Qiu, Cihang Xie, Sarah Adel Bargal, Alan Yuille, Stan Sclaroff

In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios.

BIG-bench Machine Learning Face Recognition

Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning

1 code implementation1 Jun 2021 Ju He, Adam Kortylewski, Shaokang Yang, Shuai Liu, Cheng Yang, Changhu Wang, Alan Yuille

In particular, we decouple the training of the representation and the classifier, and systematically investigate the effects of different data re-sampling techniques when training the whole network including a classifier as well as fine-tuning the feature extractor only.

A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation

1 code implementation ICCV 2021 Jiteng Mu, Weichao Qiu, Adam Kortylewski, Alan Yuille, Nuno Vasconcelos, Xiaolong Wang

To deal with the large shape variance, we introduce Articulated Signed Distance Functions (A-SDF) to represent articulated shapes with a disentangled latent space, where we have separate codes for encoding shape and articulation.

TransFG: A Transformer Architecture for Fine-grained Recognition

2 code implementations14 Mar 2021 Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang

Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences.

Fine-Grained Image Classification

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

1 code implementation22 Feb 2021 Prakhar Kaushik, Alex Gain, Adam Kortylewski, Alan Yuille

Additionally, current approaches that deal with forgetting ignore the problem of catastrophic remembering, i. e. the worsening ability to discriminate between data from different tasks.

Continual Learning

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

1 code implementation ICLR 2021 Angtian Wang, Adam Kortylewski, Alan Yuille

Using differentiable rendering we estimate the 3D object pose by minimizing the reconstruction error between NeMo and the feature representation of the target image.

3D Pose Estimation Contrastive Learning

CORL: Compositional Representation Learning for Few-Shot Classification

no code implementations28 Jan 2021 Ju He, Adam Kortylewski, Alan Yuille

In particular, during meta-learning, we train a knowledge base that consists of a dictionary of component representations and a dictionary of component activation maps that encode common spatial activation patterns of components.

Classification Few-Shot Image Classification +3

Unsupervised Part Discovery via Feature Alignment

no code implementations1 Dec 2020 Mengqi Guo, Yutong Bai, Zhishuai Zhang, Adam Kortylewski, Alan Yuille

Specifically, given a training image, we find a set of similar images that show instances of the same object category in the same pose, through an affine alignment of their corresponding feature maps.

Object Recognition

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

no code implementations1 Dec 2020 Christian Cosgrove, Adam Kortylewski, Chenglin Yang, Alan Yuille

Second, we find that compositional deep networks, which have part-based representations that lead to innate robustness to natural occlusion, are robust to patch attacks on PASCAL3D+ and the German Traffic Sign Recognition Benchmark, without adversarial training.

Traffic Sign Recognition

Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model

1 code implementation CVPR 2022 Yihong Sun, Adam Kortylewski, Alan Yuille

Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries.

Amodal Instance Segmentation Out-of-Distribution Generalization +1

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

no code implementations28 Jun 2020 Adam Kortylewski, Qing Liu, Angtian Wang, Yihong Sun, Alan Yuille

The structure of the compositional model enables CompositionalNets to decompose images into objects and context, as well as to further decompose object representations in terms of individual parts and the objects' pose.

Image Classification object-detection +2

Robust Object Detection under Occlusion with Context-Aware CompositionalNets

no code implementations CVPR 2020 Angtian Wang, Yihong Sun, Adam Kortylewski, Alan Yuille

In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself.

object-detection Robust Object Detection

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

1 code implementation CVPR 2020 Adam Kortylewski, Ju He, Qing Liu, Alan Yuille

Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion.

General Classification

Localizing Occluders with Compositional Convolutional Networks

no code implementations18 Nov 2019 Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, Alan Yuille

Our experimental results demonstrate that the proposed extensions increase the model's performance at localizing occluders as well as at classifying partially occluded objects.

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

no code implementations9 Sep 2019 Mingqing Xiao, Adam Kortylewski, Ruihai Wu, Siyuan Qiao, Wei Shen, Alan Yuille

Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.

General Classification Object Recognition

Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion

no code implementations28 May 2019 Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, Alan Yuille

In this work, we combine DCNNs and compositional object models to retain the best of both approaches: a discriminative model that is robust to partial occlusion and mask attacks.

General Classification Image Classification

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

no code implementations21 Mar 2019 Ilke Demir, Camilla Hahn, Kathryn Leonard, Geraldine Morin, Dana Rahbani, Athina Panotopoulou, Amelie Fondevilla, Elena Balashova, Bastien Durix, Adam Kortylewski

We present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape Understanding workshop to utilize existing and develop novel deep learning architectures for shape understanding.

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

no code implementations19 Nov 2018 Adam Kortylewski, Mario Wieser, Andreas Morel-Forster, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth, Thomas Vetter

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects.

Bayesian Inference

Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?

1 code implementation19 Nov 2018 Adam Kortylewski, Bernhard Egger, Andreas Morel-Forster, Andreas Schneider, Thomas Gerig, Clemens Blumer, Corius Reyneke, Thomas Vetter

We observe the following positive effects for face recognition and facial landmark detection tasks: 1) Priming with synthetic face images improves the performance consistently across all benchmarks because it reduces the negative effects of biases in the training data.

Data Augmentation Face Model +3

Training Deep Face Recognition Systems with Synthetic Data

2 code implementations16 Feb 2018 Adam Kortylewski, Andreas Schneider, Thomas Gerig, Bernhard Egger, Andreas Morel-Forster, Thomas Vetter

In our experiments with an off-the-shelf face recognition software we observe the following phenomena: 1) The amount of real training data needed to train competitive deep face recognition systems can be reduced significantly.

Face Model Face Recognition

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

2 code implementations5 Dec 2017 Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster, Thomas Vetter

4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation.

Face Recognition

Greedy Structure Learning of Hierarchical Compositional Models

no code implementations CVPR 2019 Adam Kortylewski, Aleksander Wieczorek, Mario Wieser, Clemens Blumer, Sonali Parbhoo, Andreas Morel-Forster, Volker Roth, Thomas Vetter

In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter.

Transfer Learning

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