no code implementations • 8 Apr 2024 • Fengrui Tian, Yaoyao Liu, Adam Kortylewski, Yueqi Duan, Shaoyi Du, Alan Yuille, Angtian Wang
Instead of using manually annotated images, we leverage diffusion models (e. g., Zero-1-to-3) to generate a set of images under controlled pose differences and propose to learn our object pose estimator with those images.
no code implementations • 12 Mar 2024 • Prakhar Kaushik, Adam Kortylewski, Alan Yuille
This enables us to learn a transitional dictionary of vMF kernels that are intermediate between the source and target domains and train the generative model on this dictionary using the annotations on the source domain, followed by iterative refinement.
no code implementations • 19 Jan 2024 • Prakhar Kaushik, Aayush Mishra, Adam Kortylewski, Alan Yuille
We focus on individual locally robust mesh vertex features and iteratively update them based on their proximity to corresponding features in the target domain even when the global pose is not correct.
Ranked #1 on Unsupervised Domain Adaptation on OOD-CV
no code implementations • 18 Dec 2023 • Diogo Luvizon, Vladislav Golyanik, Adam Kortylewski, Marc Habermann, Christian Theobalt
Creating a digital human avatar that is relightable, drivable, and photorealistic is a challenging and important problem in Vision and Graphics.
1 code implementation • 10 Dec 2023 • Haokai Pang, Heming Zhu, Adam Kortylewski, Christian Theobalt, Marc Habermann
Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics.
1 code implementation • 30 Nov 2023 • Ruxiao Duan, Yaoyao Liu, Jieneng Chen, Adam Kortylewski, Alan Yuille
Replay-based methods in class-incremental learning (CIL) have attained remarkable success, as replaying the exemplars of old classes can significantly mitigate catastrophic forgetting.
no code implementations • 18 Nov 2023 • Yu Chi, Fangneng Zhan, Sibo Wu, Christian Theobalt, Adam Kortylewski
The generated data is applicable across various computer vision tasks, including video segmentation and 3D point cloud segmentation.
2 code implementations • NeurIPS 2023 • Xingrui Wang, Wufei Ma, Zhuowan Li, Adam Kortylewski, Alan Yuille
In this work, we introduce the task of 3D-aware VQA, which focuses on challenging questions that require a compositional reasoning over the 3D structure of visual scenes.
no code implementations • ICCV 2023 • Jiacong Xu, Yi Zhang, Jiawei Peng, Wufei Ma, Artur Jesslen, Pengliang Ji, Qixin Hu, Jiehua Zhang, Qihao Liu, Jiahao Wang, Wei Ji, Chen Wang, Xiaoding Yuan, Prakhar Kaushik, Guofeng Zhang, Jie Liu, Yushan Xie, Yawen Cui, Alan Yuille, Adam Kortylewski
Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model.
Ranked #1 on Animal Pose Estimation on Animal3D
1 code implementation • ICCV 2023 • Yi Zhang, Pengliang Ji, Angtian Wang, Jieru Mei, Adam Kortylewski, Alan Yuille
Motivated by the recent success of generative models in rigid object pose estimation, we propose 3D-aware Neural Body Fitting (3DNBF) - an approximate analysis-by-synthesis approach to 3D human pose estimation with SOTA performance and occlusion robustness.
no code implementations • 13 Jun 2023 • Wufei Ma, Qihao Liu, Jiahao Wang, Angtian Wang, Xiaoding Yuan, Yi Zhang, Zihao Xiao, Guofeng Zhang, Beijia Lu, Ruxiao Duan, Yongrui Qi, Adam Kortylewski, Yaoyao Liu, Alan Yuille
With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically.
no code implementations • 1 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.
no code implementations • 1 Jun 2023 • Qihao Liu, Adam Kortylewski, Yutong Bai, Song Bai, Alan Yuille
(2) We find regions in the latent space that lead to distorted images independent of the text prompt, suggesting that parts of the latent space are not well-structured.
no code implementations • 31 May 2023 • Angtian Wang, Wufei Ma, Alan Yuille, Adam Kortylewski
Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios.
no code implementations • 25 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.
no code implementations • 24 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.
1 code implementation • 5 May 2023 • Fangneng Zhan, Lingjie Liu, Adam Kortylewski, Christian Theobalt
In this work, we extend this problem to a general paradigm with a taxonomy of discrete \& continuous cases, and develop a learning framework to jointly optimize gauge transformations and neural fields.
no code implementations • 17 Apr 2023 • Bingchen Zhao, Jiahao Wang, Wufei Ma, Artur Jesslen, Siwei Yang, Shaozuo Yu, Oliver Zendel, Christian Theobalt, Alan Yuille, Adam Kortylewski
Enhancing the robustness of vision algorithms in real-world scenarios is challenging.
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.
1 code implementation • 12 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.
2 code implementations • CVPR 2023 • Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, Alan Yuille
Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization.
no code implementations • 27 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.
no code implementations • 4 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.
1 code implementation • 12 Sep 2022 • Wufei Ma, Angtian Wang, Alan Yuille, Adam Kortylewski
We consider the problem of category-level 6D pose estimation from a single RGB image.
1 code implementation • 30 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.
1 code implementation • CVPR 2022 • Vipul Gupta, Zhuowan Li, Adam Kortylewski, Chenyu Zhang, Yingwei Li, Alan Yuille
By swapping the context object features, the model reliance on context can be suppressed effectively.
2 code implementations • 27 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.
1 code implementation • 2 Dec 2021 • Ju He, Shuo Yang, Shaokang Yang, Adam Kortylewski, Xiaoding Yuan, Jie-Neng Chen, Shuai Liu, Cheng Yang, Qihang Yu, Alan Yuille
To help address this problem, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations.
no code implementations • 29 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.
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.
no code implementations • 26 Oct 2021 • Yixiao Zhang, Adam Kortylewski, Qing Liu, Seyoun Park, Benjamin Green, Elizabeth Engle, Guillermo Almodovar, Ryan Walk, Sigfredo Soto-Diaz, Janis Taube, Alex Szalay, Alan Yuille
It only requires annotations on isolated nucleus, rather than on all nuclei in the dataset.
3 code implementations • 12 Jul 2021 • Chenglin Yang, Siyuan Qiao, Adam Kortylewski, Alan Yuille
Self-Attention has become prevalent in computer vision models.
1 code implementation • CVPR 2023 • Chunlu Li, Andreas Morel-Forster, Thomas Vetter, Bernhard Egger, Adam Kortylewski
The main challenge is that the model fitting and the outlier segmentation are mutually dependent on each other, and need to be inferred jointly.
Ranked #4 on 3D Face Reconstruction on NoW Benchmark
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.
1 code implementation • 1 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.
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.
1 code implementation • CVPR 2022 • Qing Liu, Adam Kortylewski, Zhishuai Zhang, Zizhang Li, Mengqi Guo, Qihao Liu, Xiaoding Yuan, Jiteng Mu, Weichao Qiu, Alan Yuille
We believe our dataset provides a rich testbed to study UDA for part segmentation and will help to significantly push forward research in this area.
2 code implementations • 14 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.
Ranked #4 on Fine-Grained Image Classification on CUB-200-2011
1 code implementation • 22 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.
Ranked #1 on Continual Learning on ImageNet-50 (5 tasks)
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.
no code implementations • 28 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.
1 code implementation • CVPR 2021 • Xiaoding Yuan, Adam Kortylewski, Yihong Sun, Alan Yuille
The improved segmentation masks are, in turn, integrated into the network in a top-down manner to improve the image classification.
no code implementations • 1 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.
no code implementations • 1 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.
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.
no code implementations • 29 Sep 2020 • Yutong Bai, Angtian Wang, Adam Kortylewski, Alan Yuille
In this paper, we introduce a contrastive learning framework for keypoint detection (CoKe).
no code implementations • 28 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.
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.
2 code implementations • ECCV 2020 • Chenglin Yang, Adam Kortylewski, Cihang Xie, Yinzhi Cao, Alan Yuille
PatchAttack induces misclassifications by superimposing small textured patches on the input image.
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.
no code implementations • 18 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.
no code implementations • 9 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.
1 code implementation • 3 Sep 2019 • Bernhard Egger, William A. P. Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, Christian Theobalt, Volker Blanz, Thomas Vetter
In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed.
no code implementations • 28 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.
no code implementations • 21 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.
no code implementations • 19 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.
1 code implementation • 19 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.
2 code implementations • 16 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.
2 code implementations • 5 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.
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.