2 code implementations • CVPR 2014 • Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, Bernt Schiele
Human pose estimation has made significant progress during the last years.
2 code implementations • CVPR 2018 • Mykhaylo Andriluka, Umar Iqbal, Eldar Insafutdinov, Leonid Pishchulin, Anton Milan, Juergen Gall, Bernt Schiele
In this work, we aim to further advance the state of the art by establishing "PoseTrack", a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis.
Ranked #3 on Multi-Person Pose Estimation on PoseTrack2017
3 code implementations • CVPR 2023 • Haiyang Wang, Chen Shi, Shaoshuai Shi, Meng Lei, Sen Wang, Di He, Bernt Schiele, LiWei Wang
However, due to the sparse characteristics of point clouds, it is non-trivial to apply a standard transformer on sparse points.
Ranked #1 on 3D Object Detection on nuScenes LiDAR only
16 code implementations • 10 May 2016 • Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres, Mykhaylo Andriluka, Bernt Schiele
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people.
Ranked #1 on Multi-Person Pose Estimation on WAF
40 code implementations • 17 May 2016 • Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal.
4 code implementations • 15 May 2022 • Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu, Jindong Wang, Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt Schiele, Xing Xie
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
4 code implementations • 26 Jan 2023 • Hao Chen, Ran Tao, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Bhiksha Raj, Marios Savvides
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
4 code implementations • 12 Aug 2022 • Yidong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wenxin Hou, RenJie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, Heli Qi, Zhen Wu, Yu-Feng Li, Satoshi Nakamura, Wei Ye, Marios Savvides, Bhiksha Raj, Takahiro Shinozaki, Bernt Schiele, Jindong Wang, Xing Xie, Yue Zhang
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
14 code implementations • CVPR 2017 • Eldar Insafutdinov, Mykhaylo Andriluka, Leonid Pishchulin, Siyu Tang, Evgeny Levinkov, Bjoern Andres, Bernt Schiele
In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos.
Ranked #7 on Keypoint Detection on MPII Multi-Person
9 code implementations • CVPR 2016 • Scott Reed, Zeynep Akata, Bernt Schiele, Honglak Lee
State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information.
2 code implementations • CVPR 2019 • Qianru Sun, Yaoyao Liu, Tat-Seng Chua, Bernt Schiele
In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks.
1 code implementation • 7 Oct 2019 • Qianru Sun, Yaoyao Liu, Zhaozheng Chen, Tat-Seng Chua, Bernt Schiele
In this paper, we propose a novel approach called meta-transfer learning (MTL) which learns to transfer the weights of a deep NN for few-shot learning tasks.
3 code implementations • NeurIPS 2021 • Yaoyao Liu, Bernt Schiele, Qianru Sun
Class-Incremental Learning (CIL) [40] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase.
1 code implementation • 20 Sep 2022 • Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele
In this report, we present the 1st place solution for motion prediction track in 2022 Waymo Open Dataset Challenges.
2 code implementations • 27 Sep 2022 • Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele
Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to make safe decisions.
1 code implementation • 30 Jun 2023 • Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele
Extensive experimental results demonstrate that the MTR framework achieves state-of-the-art performance on the highly-competitive motion prediction benchmarks, while the MTR++ framework surpasses its precursor, exhibiting enhanced performance and efficiency in predicting accurate multimodal future trajectories for multiple agents.
2 code implementations • CVPR 2020 • Yaoyao Liu, Yu-Ting Su, An-An Liu, Bernt Schiele, Qianru Sun
However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones.
2 code implementations • CVPR 2021 • Yaoyao Liu, Bernt Schiele, Qianru Sun
Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase.
2 code implementations • 17 Aug 2018 • Mohamed Omran, Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler, Bernt Schiele
Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models.
Ranked #1 on Monocular 3D Human Pose Estimation on Human3.6M (Use Video Sequence metric)
3 code implementations • 28 Feb 2020 • Edgar Schönfeld, Bernt Schiele, Anna Khoreva
The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism.
Ranked #1 on Image Generation on CelebA 128x128
2 code implementations • ICCV 2023 • Haiyang Wang, Hao Tang, Shaoshuai Shi, Aoxue Li, Zhenguo Li, Bernt Schiele, LiWei Wang
Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems.
Ranked #8 on 3D Object Detection on nuScenes
1 code implementation • ICLR 2021 • Vadim Sushko, Edgar Schönfeld, Dan Zhang, Juergen Gall, Bernt Schiele, Anna Khoreva
By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity with better alignment to their input label maps, making the use of the perceptual loss superfluous.
1 code implementation • 14 Nov 2016 • Evgeny Levinkov, Jonas Uhrig, Siyu Tang, Mohamed Omran, Eldar Insafutdinov, Alexander Kirillov, Carsten Rother, Thomas Brox, Bernt Schiele, Bjoern Andres
In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time.
3 code implementations • 12 Nov 2015 • Anna Rohrbach, Marcus Rohrbach, Ronghang Hu, Trevor Darrell, Bernt Schiele
We propose a novel approach which learns grounding by reconstructing a given phrase using an attention mechanism, which can be either latent or optimized directly.
Ranked #12 on Phrase Grounding on Flickr30k Entities Test
4 code implementations • CVPR 2016 • Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter Gehler, Bernt Schiele
This paper considers the task of articulated human pose estimation of multiple people in real world images.
Ranked #2 on Multi-Person Pose Estimation on WAF
2 code implementations • 14 Mar 2024 • Haiyang Wang, Hao Tang, Li Jiang, Shaoshuai Shi, Muhammad Ferjad Naeem, Hongsheng Li, Bernt Schiele, LiWei Wang
Due to its simple design, this paradigm holds promise for narrowing the architectural gap between vision and language.
2 code implementations • CVPR 2019 • Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
We formulate model functionality stealing as a two-step approach: (i) querying a set of input images to the blackbox model to obtain predictions; and (ii) training a "knockoff" with queried image-prediction pairs.
1 code implementation • CVPR 2018 • Liqian Ma, Qianru Sun, Stamatios Georgoulis, Luc van Gool, Bernt Schiele, Mario Fritz
Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information.
Ranked #2 on Gesture-to-Gesture Translation on Senz3D
2 code implementations • ECCV 2020 • Garrick Brazil, Gerard Pons-Moll, Xiaoming Liu, Bernt Schiele
In this work, we propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization.
Ranked #6 on 3D Object Detection on Rope3D
1 code implementation • 26 Jun 2014 • Jan Hosang, Rodrigo Benenson, Bernt Schiele
Current top performing Pascal VOC object detectors employ detection proposals to guide the search for objects thereby avoiding exhaustive sliding window search across images.
3 code implementations • 29 Nov 2018 • Haoran Wang, Yue Fan, Zexin Wang, Licheng Jiao, Bernt Schiele
We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model.
Ranked #20 on Person Re-Identification on DukeMTMC-reID
4 code implementations • 28 Mar 2017 • Anna Khoreva, Rodrigo Benenson, Eddy Ilg, Thomas Brox, Bernt Schiele
Our approach is suitable for both single and multiple object segmentation.
2 code implementations • CVPR 2017 • Anna Khoreva, Federico Perazzi, Rodrigo Benenson, Bernt Schiele, Alexander Sorkine-Hornung
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentation.
Ranked #6 on Semi-Supervised Video Object Segmentation on YouTube
1 code implementation • CVPR 2022 • Zetong Yang, Li Jiang, Yanan sun, Bernt Schiele, Jiaya Jia
This is achieved by introducing an intermediate representation, i. e., Q-representation, in the querying stage to serve as a bridge between the embedding stage and task heads.
Ranked #7 on Semantic Segmentation on S3DIS
1 code implementation • CVPR 2022 • Moritz Böhle, Mario Fritz, Bernt Schiele
We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training.
1 code implementation • CVPR 2022 • Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc van Gool, Bernt Schiele, Federico Tombari, Fisher Yu
Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems.
1 code implementation • NeurIPS 2019 • Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele
On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning.
9 code implementations • 3 Jul 2017 • Yongqin Xian, Christoph H. Lampert, Bernt Schiele, Zeynep Akata
Due to the importance of zero-shot learning, i. e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily.
1 code implementation • CVPR 2022 • Andreas Döring, Di Chen, Shanshan Zhang, Bernt Schiele, Jürgen Gall
Current research evaluates person search, multi-object tracking and multi-person pose estimation as separate tasks and on different datasets although these tasks are very akin to each other and comprise similar sub-tasks, e. g. person detection or appearance-based association of detected persons.
3 code implementations • ICCV 2017 • Rakshith Shetty, Marcus Rohrbach, Lisa Anne Hendricks, Mario Fritz, Bernt Schiele
While strong progress has been made in image captioning over the last years, machine and human captions are still quite distinct.
1 code implementation • CVPR 2022 • Pei Wang, Zhaowei Cai, Hao Yang, Gurumurthy Swaminathan, Nuno Vasconcelos, Bernt Schiele, Stefano Soatto
This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection.
Ranked #14 on Semi-Supervised Object Detection on COCO 2% labeled data
2 code implementations • CVPR 2015 • Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, Bernt Schiele
Image classification has advanced significantly in recent years with the availability of large-scale image sets.
2 code implementations • CVPR 2017 • Shanshan Zhang, Rodrigo Benenson, Bernt Schiele
Convnets have enabled significant progress in pedestrian detection recently, but there are still open questions regarding suitable architectures and training data.
Ranked #14 on Pedestrian Detection on Caltech
3 code implementations • ICLR 2018 • Seong Joon Oh, Max Augustin, Bernt Schiele, Mario Fritz
On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -- we show that the revealed internal information helps generate more effective adversarial examples against the black box model.
1 code implementation • CVPR 2018 • Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, Marcus Rohrbach
We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths.
1 code implementation • CVPR 2017 • Qianru Sun, Bernt Schiele, Mario Fritz
Social relations are the foundation of human daily life.
Ranked #5 on Visual Social Relationship Recognition on PIPA
1 code implementation • ECCV 2020 • Yaoyao Liu, Bernt Schiele, Qianru Sun
"Empirical" means that the hyperparameters, e. g., used for learning and ensembling the epoch-wise models, are generated by hyperprior learners conditional on task-specific data.
1 code implementation • CVPR 2022 • Yue Fan, Dengxin Dai, Anna Kukleva, Bernt Schiele
In this paper, we propose a novel co-learning framework (CoSSL) with decoupled representation learning and classifier learning for imbalanced SSL.
1 code implementation • 27 Sep 2019 • Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success.
4 code implementations • CVPR 2018 • Yongqin Xian, Tobias Lorenz, Bernt Schiele, Zeynep Akata
Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task.
Ranked #5 on Generalized Zero-Shot Learning on SUN Attribute
Generalized Zero-Shot Learning Generative Adversarial Network
2 code implementations • CVPR 2019 • David Stutz, Matthias Hein, Bernt Schiele
A recent hypothesis even states that both robust and accurate models are impossible, i. e., adversarial robustness and generalization are conflicting goals.
3 code implementations • ICML 2020 • David Stutz, Matthias Hein, Bernt Schiele
Our confidence-calibrated adversarial training (CCAT) tackles this problem by biasing the model towards low confidence predictions on adversarial examples.
1 code implementation • 5 May 2020 • Sukrut Rao, David Stutz, Bernt Schiele
Then, we apply adversarial training on these location-optimized adversarial patches and demonstrate significantly improved robustness on CIFAR10 and GTSRB.
1 code implementation • CVPR 2022 • Haiyang Wang, Shaoshuai Shi, Ze Yang, Rongyao Fang, Qi Qian, Hongsheng Li, Bernt Schiele, LiWei Wang
In order to learn better representations of object shape to enhance cluster features for predicting 3D boxes, we propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays uniformly emitted from cluster centers.
Ranked #12 on 3D Object Detection on ScanNetV2
1 code implementation • CVPR 2022 • Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata
Our model visually divides a set of images from seen classes into clusters of local image regions according to their visual similarity, and further imposes their class discrimination and semantic relatedness.
1 code implementation • 12 Dec 2016 • Maksim Lapin, Matthias Hein, Bernt Schiele
In particular, we find that it is possible to obtain effective multilabel classifiers on Pascal VOC using a single label per image for training, while the gap between multiclass and multilabel methods on MS COCO is more significant.
1 code implementation • CVPR 2016 • Maksim Lapin, Matthias Hein, Bernt Schiele
In the experiments, we compare on various datasets all of the proposed and established methods for top-k error optimization.
1 code implementation • NeurIPS 2015 • Maksim Lapin, Matthias Hein, Bernt Schiele
Class ambiguity is typical in image classification problems with a large number of classes.
1 code implementation • CVPR 2023 • Jian Ding, Nan Xue, Gui-Song Xia, Bernt Schiele, Dengxin Dai
This work studies semantic segmentation under the domain generalization setting, where a model is trained only on the source domain and tested on the unseen target domain.
1 code implementation • 19 Jun 2023 • Moritz Böhle, Navdeeppal Singh, Mario Fritz, Bernt Schiele
We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training.
1 code implementation • 17 Aug 2022 • Zhi Li, Soshi Shimada, Bernt Schiele, Christian Theobalt, Vladislav Golyanik
3D human motion capture from monocular RGB images respecting interactions of a subject with complex and possibly deformable environments is a very challenging, ill-posed and under-explored problem.
1 code implementation • ICCV 2023 • Zixu Zhao, Jiaze Wang, Max Horn, Yizhuo Ding, Tong He, Zechen Bai, Dominik Zietlow, Carl-Johann Simon-Gabriel, Bing Shuai, Zhuowen Tu, Thomas Brox, Bernt Schiele, Yanwei Fu, Francesco Locatello, Zheng Zhang, Tianjun Xiao
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines.
1 code implementation • CVPR 2021 • Moritz Böhle, Mario Fritz, Bernt Schiele
Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns.
1 code implementation • 27 Sep 2021 • Moritz Böhle, Mario Fritz, Bernt Schiele
As a result, CoDA Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions.
1 code implementation • CVPR 2023 • Zilin Luo, Yaoyao Liu, Bernt Schiele, Qianru Sun
Exemplar-based class-incremental learning (CIL) finetunes the model with all samples of new classes but few-shot exemplars of old classes in each incremental phase, where the "few-shot" abides by the limited memory budget.
1 code implementation • 7 Oct 2023 • Nina Shvetsova, Anna Kukleva, Xudong Hong, Christian Rupprecht, Bernt Schiele, Hilde Kuehne
Specifically, we prompt an LLM to create plausible video descriptions based on ASR narrations of the video for a large-scale instructional video dataset.
1 code implementation • 23 Mar 2023 • Anna Kukleva, Moritz Böhle, Bernt Schiele, Hilde Kuehne, Christian Rupprecht
Such a schedule results in a constant `task switching' between an emphasis on instance discrimination and group-wise discrimination and thereby ensures that the model learns both group-wise features, as well as instance-specific details.
1 code implementation • 3 Jan 2024 • Haopeng Li, Andong Deng, Qiuhong Ke, Jun Liu, Hossein Rahmani, Yulan Guo, Bernt Schiele, Chen Chen
Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval.
1 code implementation • ICCV 2021 • Farzaneh Rezaeianaran, Rakshith Shetty, Rahaf Aljundi, Daniel Olmeda Reino, Shanshan Zhang, Bernt Schiele
In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate new data.
Multi-Source Unsupervised Domain Adaptation Object Detection +1
1 code implementation • 11 Jan 2023 • Yaoyao Liu, YingYing Li, Bernt Schiele, Qianru Sun
Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase.
1 code implementation • 21 Mar 2023 • Sukrut Rao, Moritz Böhle, Amin Parchami-Araghi, Bernt Schiele
To gain a better understanding of which model-guiding approaches actually transfer to more challenging real-world datasets, in this work we conduct an in-depth evaluation across various loss functions, attribution methods, models, and 'guidance depths' on the PASCAL VOC 2007 and MS COCO 2014 datasets, and show that model guidance can sometimes even improve model performance.
1 code implementation • ICCV 2023 • Sukrut Rao, Moritz Böhle, Amin Parchami-Araghi, Bernt Schiele
To better understand the effectiveness of the various design choices that have been explored in the context of model guidance, in this work we conduct an in-depth evaluation across various loss functions, attribution methods, models, and 'guidance depths' on the PASCAL VOC 2007 and MS COCO 2014 datasets.
1 code implementation • CVPR 2022 • Sukrut Rao, Moritz Böhle, Bernt Schiele
Finally, we propose a post-processing smoothing step that significantly improves the performance of some attribution methods, and discuss its applicability.
1 code implementation • ICCV 2023 • Nina Shvetsova, Felix Petersen, Anna Kukleva, Bernt Schiele, Hilde Kuehne
Contrastive learning has become an important tool in learning representations from unlabeled data mainly relying on the idea of minimizing distance between positive data pairs, e. g., views from the same images, and maximizing distance between negative data pairs, e. g., views from different images.
1 code implementation • 21 Mar 2023 • Sukrut Rao, Moritz Böhle, Bernt Schiele
Finally, we propose a post-processing smoothing step that significantly improves the performance of some attribution methods, and discuss its applicability.
1 code implementation • CVPR 2020 • Shweta Mahajan, Apratim Bhattacharyya, Mario Fritz, Bernt Schiele, Stefan Roth
Flow-based generative models are an important class of exact inference models that admit efficient inference and sampling for image synthesis.
1 code implementation • ICCV 2021 • Anna Kukleva, Hilde Kuehne, Bernt Schiele
Both generalized and incremental few-shot learning have to deal with three major challenges: learning novel classes from only few samples per class, preventing catastrophic forgetting of base classes, and classifier calibration across novel and base classes.
1 code implementation • CVPR 2023 • Anurag Das, Yongqin Xian, Dengxin Dai, Bernt Schiele
In this work, we propose a common framework to use different weak labels, e. g. image, point and coarse labels from target domain to reduce this performance gap.
1 code implementation • ICCV 2023 • Nina Shvetsova, Anna Kukleva, Bernt Schiele, Hilde Kuehne
Large-scale noisy web image-text datasets have been proven to be efficient for learning robust vision-language models.
1 code implementation • ICCV 2023 • Mattia Segu, Bernt Schiele, Fisher Yu
However, the nature of a MOT system is manifold - requiring object detection and instance association - and adapting all its components is non-trivial.
1 code implementation • CVPR 2016 • Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications.
1 code implementation • 20 Jun 2018 • Apratim Bhattacharyya, Bernt Schiele, Mario Fritz
For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.
Ranked #6 on Human Pose Forecasting on Human3.6M (ADE metric)
1 code implementation • 9 Jul 2020 • Yongqin Xian, Bruno Korbar, Matthijs Douze, Lorenzo Torresani, Bernt Schiele, Zeynep Akata
Few-shot learning aims to recognize novel classes from a few examples.
1 code implementation • 19 Jul 2022 • Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello
Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e. g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations.
Adversarial Robustness Out-of-Distribution Generalization +1
1 code implementation • ICCV 2023 • Yong Guo, David Stutz, Bernt Schiele
Interestingly, we observe that the attention mechanism of ViTs tends to rely on few important tokens, a phenomenon we call token overfocusing.
1 code implementation • 5 Feb 2024 • Amin Parchami-Araghi, Moritz Böhle, Sukrut Rao, Bernt Schiele
Knowledge Distillation (KD) has proven effective for compressing large teacher models into smaller student models.
1 code implementation • ECCV 2020 • Yang He, Shadi Rahimian, Bernt Schiele, Mario Fritz
Today's success of state of the art methods for semantic segmentation is driven by large datasets.
1 code implementation • ICLR 2019 • Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence.
1 code implementation • 16 Nov 2021 • Di Chen, Andreas Doering, Shanshan Zhang, Jian Yang, Juergen Gall, Bernt Schiele
Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras.
Representation Learning Video-Based Person Re-Identification
1 code implementation • CVPR 2022 • Shenjian Gong, Shanshan Zhang, Jian Yang, Dengxin Dai, Bernt Schiele
The main challenge for this task is to achieve high-quality manual annotations on a large amount of training data.
1 code implementation • CVPR 2023 • Yong Guo, David Stutz, Bernt Schiele
Despite their success, vision transformers still remain vulnerable to image corruptions, such as noise or blur.
2 code implementations • NeurIPS 2017 • Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc van Gool
This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose.
Ranked #3 on Gesture-to-Gesture Translation on Senz3D
1 code implementation • 6 Sep 2017 • Yang He, Margret Keuper, Bernt Schiele, Mario Fritz
In this paper, we present an approach for learning dilation parameters adaptively per channel, consistently improving semantic segmentation results on street-scene datasets like Cityscapes and Camvid.
1 code implementation • ECCV 2018 • Yang He, Bernt Schiele, Mario Fritz
Recent advances in Deep Learning and probabilistic modeling have led to strong improvements in generative models for images.
1 code implementation • 30 Jan 2022 • Yong Guo, David Stutz, Bernt Schiele
We show that EWS greatly improves both robustness against corrupted images as well as accuracy on clean data.
1 code implementation • 7 Apr 2020 • Saurabh Sharma, Ning Yu, Mario Fritz, Bernt Schiele
Deep learning enables impressive performance in image recognition using large-scale artificially-balanced datasets.
Ranked #19 on Long-tail Learning on Places-LT
1 code implementation • 24 Jun 2020 • David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele
Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly, however, causes bit-level failures in the memory storing the quantized DNN weights.
1 code implementation • 12 Sep 2023 • Max Losch, David Stutz, Bernt Schiele, Mario Fritz
In this paper, we propose a Calibrated Lipschitz-Margin Loss (CLL) that addresses this issue and improves certified robustness by tackling two problems: Firstly, commonly used margin losses do not adjust the penalties to the shrinking output distribution; caused by minimizing the Lipschitz constant $K$.
1 code implementation • CVPR 2017 • Yongqin Xian, Bernt Schiele, Zeynep Akata
Due to the importance of zero-shot learning, the number of proposed approaches has increased steadily recently.
1 code implementation • ICCV 2023 • Yue Fan, Anna Kukleva, Dengxin Dai, Bernt Schiele
In experiments, SSB greatly improves both inlier classification and outlier detection performance, outperforming existing methods by a large margin.
no code implementations • NeurIPS 2018 • Rakshith Shetty, Mario Fritz, Bernt Schiele
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets.
no code implementations • 15 May 2018 • Tribhuvanesh Orekondy, Seong Joon Oh, Yang Zhang, Bernt Schiele, Mario Fritz
At the core of FL is a network of anonymous user devices sharing training information (model parameter updates) computed locally on personal data.
no code implementations • 21 Mar 2018 • Anna Khoreva, Anna Rohrbach, Bernt Schiele
We show that our language-supervised approach performs on par with the methods which have access to a pixel-level mask of the target object on DAVIS'16 and is competitive to methods using scribbles on the challenging DAVIS'17 dataset.
Ranked #1 on Video Object Segmentation on DAVIS 2017 (mIoU metric)
no code implementations • 21 Apr 2018 • Mihai Fieraru, Anna Khoreva, Leonid Pishchulin, Bernt Schiele
Multi-person pose estimation in images and videos is an important yet challenging task with many applications.
no code implementations • ECCV 2018 • Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, Bernt Schiele
As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition becomes increasingly challenging.
no code implementations • CVPR 2018 • Qianru Sun, Liqian Ma, Seong Joon Oh, Luc van Gool, Bernt Schiele, Mario Fritz
As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection.
no code implementations • 6 Nov 2017 • Rakshith Shetty, Bernt Schiele, Mario Fritz
In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ($A^4NT$), to combat such text-based adversaries.
no code implementations • CVPR 2018 • Tribhuvanesh Orekondy, Mario Fritz, Bernt Schiele
Images convey a broad spectrum of personal information.
no code implementations • CVPR 2018 • Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Our experimental results show that it is indeed possible to predict people trajectories at the desired time horizons and that our uncertainty estimates are informative of the prediction error.
Ranked #5 on Trajectory Prediction on JAAD
no code implementations • 27 Nov 2016 • Apratim Bhattacharyya, Mateusz Malinowski, Bernt Schiele, Mario Fritz
Boundary estimation in images and videos has been a very active topic of research, and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception.
no code implementations • 17 Nov 2017 • Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, Marcus Rohrbach
We also introduce a multimodal methodology for generating visual and textual explanations simultaneously.
no code implementations • 9 Oct 2017 • Seong Joon Oh, Rodrigo Benenson, Mario Fritz, Bernt Schiele
Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e. g. backward viewpoints, unusual poses) and great changes in appearance.
no code implementations • ICCV 2017 • Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
Third, we propose models that predict user specific privacy score from images in order to enforce the users' privacy preferences.
no code implementations • ICCV 2017 • Seong Joon Oh, Mario Fritz, Bernt Schiele
We derive the optimal strategy for the user that assures an upper bound on the recognition rate independent of the recogniser's counter measure.
no code implementations • 14 Dec 2016 • Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Bernt Schiele, Trevor Darrell, Marcus Rohrbach
In contrast, humans can justify their decisions with natural language and point to the evidence in the visual world which led to their decisions.
no code implementations • CVPR 2017 • Seong Joon Oh, Rodrigo Benenson, Anna Khoreva, Zeynep Akata, Mario Fritz, Bernt Schiele
We show how to combine both information sources in order to recover 80% of the fully supervised performance - which is the new state of the art in weakly supervised training for pixel-wise semantic labelling.
Ranked #26 on Semantic Segmentation on PASCAL VOC 2012 val
no code implementations • CVPR 2017 • Jan Hosang, Rodrigo Benenson, Bernt Schiele
Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, features, and the classifier becoming one neural network improved results two-fold on general object detection.
no code implementations • CVPR 2017 • Nour Karessli, Zeynep Akata, Bernt Schiele, Andreas Bulling
Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts.
no code implementations • CVPR 2017 • Anna Rohrbach, Marcus Rohrbach, Siyu Tang, Seong Joon Oh, Bernt Schiele
At training time, we first learn how to localize characters by relating their visual appearance to mentions in the descriptions via a semi-supervised approach.
no code implementations • 19 Mar 2015 • Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt, Bernt Schiele
Statistical models of 3D human shape and pose learned from scan databases have developed into valuable tools to solve a variety of vision and graphics problems.
no code implementations • 31 Dec 2016 • Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov, Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt
Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center.
no code implementations • 12 May 2016 • Anna Khoreva, Rodrigo Benenson, Fabio Galasso, Matthias Hein, Bernt Schiele
Graph-based video segmentation methods rely on superpixels as starting point.
no code implementations • CVPR 2017 • Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, Bernt Schiele
Semantic labelling and instance segmentation are two tasks that require particularly costly annotations.
Ranked #1 on Semantic Segmentation on PASCAL VOC 2012 val (Mean IoU metric)
no code implementations • NeurIPS 2016 • Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee
Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, birds, and flowers.
Ranked #13 on Text-to-Image Generation on CUB (using extra training data)
no code implementations • 23 Sep 2016 • Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov, Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt
We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual reality headset.
no code implementations • 17 Aug 2016 • Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Bernt Schiele
In [1], we proposed a graph-based formulation that links and clusters person hypotheses over time by solving a minimum cost subgraph multicut problem.
no code implementations • 28 Jul 2016 • Seong Joon Oh, Rodrigo Benenson, Mario Fritz, Bernt Schiele
As we shift more of our lives into the virtual domain, the volume of data shared on the web keeps increasing and presents a threat to our privacy.
no code implementations • 21 Jul 2016 • Margret Keuper, Siyu Tang, Yu Zhongjie, Bjoern Andres, Thomas Brox, Bernt Schiele
Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios.
no code implementations • CVPR 2016 • Shanshan Zhang, Rodrigo Benenson, Mohamed Omran, Jan Hosang, Bernt Schiele
We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector.
no code implementations • 12 May 2016 • Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, Bernt Schiele
In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions.
no code implementations • CVPR 2016 • Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, Bernt Schiele
We train the model with a ranking based objective function which penalizes incorrect rankings of the true class for a given image.
no code implementations • CVPR 2016 • Zeynep Akata, Mateusz Malinowski, Mario Fritz, Bernt Schiele
A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing the new classes.
no code implementations • 28 Mar 2016 • Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, Trevor Darrell
Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself.
no code implementations • 19 Nov 2015 • Jan Hosang, Rodrigo Benenson, Bernt Schiele
Non-maximum suppression (NMS) is used in virtually all state-of-the-art object detection pipelines.
no code implementations • CVPR 2016 • Anna Khoreva, Rodrigo Benenson, Mohamed Omran, Matthias Hein, Bernt Schiele
State-of-the-art learning based boundary detection methods require extensive training data.
Ranked #2 on Edge Detection on SBD
no code implementations • NeurIPS 2015 • Pratik Jawanpuria, Maksim Lapin, Matthias Hein, Bernt Schiele
The paradigm of multi-task learning is that one can achieve better generalization by learning tasks jointly and thus exploiting the similarity between the tasks rather than learning them independently of each other.
no code implementations • 23 Feb 2015 • Marcus Rohrbach, Anna Rohrbach, Michaela Regneri, Sikandar Amin, Mykhaylo Andriluka, Manfred Pinkal, Bernt Schiele
To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts.
no code implementations • ICCV 2015 • Gaurav Sharma, Bernt Schiele
We propose a novel algorithm for the task of supervised discriminative distance learning by nonlinearly embedding vectors into a low dimensional Euclidean space.
no code implementations • ICCV 2015 • Seong Joon Oh, Rodrigo Benenson, Mario Fritz, Bernt Schiele
Recognising persons in everyday photos presents major challenges (occluded faces, different clothing, locations, etc.)
no code implementations • 12 Aug 2015 • Bojan Pepik, Rodrigo Benenson, Tobias Ritschel, Bernt Schiele
", and "what can the network learn?".
no code implementations • 17 Feb 2015 • Jan Hosang, Rodrigo Benenson, Piotr Dollár, Bernt Schiele
Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images.
no code implementations • 4 Jun 2015 • Anna Rohrbach, Marcus Rohrbach, Bernt Schiele
Generating descriptions for videos has many applications including assisting blind people and human-robot interaction.
no code implementations • 17 Mar 2015 • Bojan Pepik, Michael Stark, Peter Gehler, Tobias Ritschel, Bernt Schiele
Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations.
no code implementations • CVPR 2015 • Jan Hosang, Mohamed Omran, Rodrigo Benenson, Bernt Schiele
In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection.
Ranked #31 on Pedestrian Detection on Caltech
no code implementations • 23 Jan 2015 • Shanshan Zhang, Rodrigo Benenson, Bernt Schiele
This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest.
Ranked #29 on Pedestrian Detection on Caltech
no code implementations • CVPR 2015 • Anna Rohrbach, Marcus Rohrbach, Niket Tandon, Bernt Schiele
In this work we propose a novel dataset which contains transcribed DVS, which is temporally aligned to full length HD movies.
no code implementations • 16 Nov 2014 • Rodrigo Benenson, Mohamed Omran, Jan Hosang, Bernt Schiele
Paper-by-paper results make it easy to miss the forest for the trees. We analyse the remarkable progress of the last decade by discussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark.
no code implementations • 7 Jun 2014 • Leonid Pishchulin, Mykhaylo Andriluka, Bernt Schiele
Holistic methods based on dense trajectories are currently the de facto standard for recognition of human activities in video.
no code implementations • 24 Mar 2014 • Anna Senina, Marcus Rohrbach, Wei Qiu, Annemarie Friedrich, Sikandar Amin, Mykhaylo Andriluka, Manfred Pinkal, Bernt Schiele
Humans can easily describe what they see in a coherent way and at varying level of detail.
no code implementations • 13 Jun 2013 • Maksim Lapin, Matthias Hein, Bernt Schiele
Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training.
no code implementations • 20 Dec 2013 • Bojan Pepik, Michael Stark, Peter Gehler, Bernt Schiele
While the majority of today's object class models provide only 2D bounding boxes, far richer output hypotheses are desirable including viewpoint, fine-grained category, and 3D geometry estimate.
no code implementations • 18 Jun 2018 • Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.
no code implementations • 17 Dec 2018 • Rakshith Shetty, Bernt Schiele, Mario Fritz
We propose a method to quantify the sensitivity of black-box vision models to visual context by editing images to remove selected objects and measuring the response of the target models.
no code implementations • NeurIPS 2013 • Marcus Rohrbach, Sandra Ebert, Bernt Schiele
Our approach consistently outperforms state-of-the-art transfer and semi-supervised approaches on all datasets.
no code implementations • CVPR 2014 • Fabio Galasso, Margret Keuper, Thomas Brox, Bernt Schiele
In contrast to previous work, the reduced graph is reweighted such that the resulting segmentation is equivalent, under certain assumptions, to that of the full graph.
no code implementations • CVPR 2018 • Shanshan Zhang, Jian Yang, Bernt Schiele
In this paper, we aim to propose a simple and compact method based on the FasterRCNN architecture for occluded pedestrian detection.
no code implementations • CVPR 2018 • Apratim Bhattacharyya, Bernt Schiele, Mario Fritz
For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.
no code implementations • CVPR 2013 • Leonid Pishchulin, Mykhaylo Andriluka, Peter Gehler, Bernt Schiele
In this paper we consider the challenging problem of articulated human pose estimation in still images.
no code implementations • CVPR 2013 • Bojan Pepikj, Michael Stark, Peter Gehler, Bernt Schiele
Despite the success of recent object class recognition systems, the long-standing problem of partial occlusion remains a major challenge, and a principled solution is yet to be found.
no code implementations • CVPR 2014 • Maksim Lapin, Bernt Schiele, Matthias Hein
The underlying idea of multitask learning is that learning tasks jointly is better than learning each task individually.
no code implementations • CVPR 2014 • Vasileios Belagiannis, Sikandar Amin, Mykhaylo Andriluka, Bernt Schiele, Nassir Navab, Slobodan Ilic
In this work, we address the problem of 3D pose estimation of multiple humans from multiple views.
Ranked #24 on 3D Multi-Person Pose Estimation on Shelf
no code implementations • CVPR 2015 • Anna Khoreva, Fabio Galasso, Matthias Hein, Bernt Schiele
Video segmentation has become an important and active research area with a large diversity of proposed approaches.
no code implementations • CVPR 2015 • Shanshan Zhang, Rodrigo Benenson, Bernt Schiele
This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest.
no code implementations • CVPR 2015 • Ahmed Elhayek, Edilson de Aguiar, Arjun Jain, Jonathan Tompson, Leonid Pishchulin, Micha Andriluka, Chris Bregler, Bernt Schiele, Christian Theobalt
Our approach unites a discriminative image-based joint detection method with a model-based generative motion tracking algorithm through a combined pose optimization energy.
no code implementations • CVPR 2015 • Siyu Tang, Bjoern Andres, Miykhaylo Andriluka, Bernt Schiele
Tracking multiple targets in a video, based on a finite set of detection hypotheses, is a persistent problem in computer vision.
no code implementations • CVPR 2017 • Siyu Tang, Mykhaylo Andriluka, Bjoern Andres, Bernt Schiele
This allows us to reward tracks that assign detections of similar appearance to the same person in a way that does not introduce implausible solutions.
no code implementations • CVPR 2017 • Evgeny Levinkov, Jonas Uhrig, Siyu Tang, Mohamed Omran, Eldar Insafutdinov, Alexander Kirillov, Carsten Rother, Thomas Brox, Bernt Schiele, Bjoern Andres
In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time.
no code implementations • CVPR 2021 • Stephan Alaniz, Diego Marcos, Bernt Schiele, Zeynep Akata
Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user.
no code implementations • TACL 2013 • Michaela Regneri, Marcus Rohrbach, Dominikus Wetzel, Stefan Thater, Bernt Schiele, Manfred Pinkal
Recent work has shown that the integration of visual information into text-based models can substantially improve model predictions, but so far only visual information extracted from static images has been used.
no code implementations • CVPR 2019 • Yongqin Xian, Saurabh Sharma, Bernt Schiele, Zeynep Akata
When labeled training data is scarce, a promising data augmentation approach is to generate visual features of unknown classes using their attributes.
Ranked #3 on Generalized Zero-Shot Learning on SUN Attribute
no code implementations • 18 Apr 2019 • Liqian Ma, Qianru Sun, Bernt Schiele, Luc van Gool
Image-to-image (I2I) translation is a pixel-level mapping that requires a large number of paired training data and often suffers from the problems of high diversity and strong category bias in image scenes.
no code implementations • ICLR 2020 • Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
We find such passive defenses ineffective against DNN stealing attacks.
no code implementations • 25 Jul 2019 • Max Losch, Mario Fritz, Bernt Schiele
Additionally we show how the activations of the SB-Layer can be used for both the interpretation of failure cases of the network as well as for confidence prediction of the resulting output.
no code implementations • 24 Aug 2019 • Apratim Bhattacharyya, Michael Hanselmann, Mario Fritz, Bernt Schiele, Christoph-Nikolas Straehle
Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world.
Ranked #10 on Trajectory Prediction on Stanford Drone
no code implementations • 5 Feb 2020 • Yue Fan, Yongqin Xian, Max Maria Losch, Bernt Schiele
In this paper, we are pushing the envelope and aim to further investigate the reliance on spatial information.
no code implementations • NeurIPS 2020 • Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata
As an additional benefit, our model points to the visual evidence of the attributes in an image, e. g. for the CUB dataset, confirming the improved attribute localization ability of our image representation.
no code implementations • 2 Sep 2020 • Yazan Abu Farha, Qiuhong Ke, Bernt Schiele, Juergen Gall
With the success of deep learning methods in analyzing activities in videos, more attention has recently been focused towards anticipating future activities.
no code implementations • ECCV 2020 • Rakshith Shetty, Mario Fritz, Bernt Schiele
Constrained adversarial optimization of object appearance through this synthesizer produces rare/difficult versions of an object which fool the target object detector.
no code implementations • 18 Sep 2020 • Yang He, Bernt Schiele, Mario Fritz
Recently, learning-based image synthesis has enabled to generate high-resolution images, either applying popular adversarial training or a powerful perceptual loss.
no code implementations • 21 Sep 2020 • Apratim Bhattacharyya, Christoph-Nikolas Straehle, Mario Fritz, Bernt Schiele
This yields an exact inference method that models trajectories at different spatio-temporal resolutions in a hierarchical manner.
no code implementations • 6 Sep 2014 • Vasileios Belagiannis, Xinchao Wang, Bernt Schiele, Pascal Fua, Slobodan Ilic, Nassir Navab
To address these challenges, we propose a temporally consistent 3D Pictorial Structures model (3DPS) for multiple human pose estimation from multiple camera views.
Ranked #16 on 3D Multi-Person Pose Estimation on Campus
no code implementations • 1 Oct 2016 • Vasileios Belagiannis, Sikandar Amin, Mykhaylo Andriluka, Bernt Schiele, Nassir Navab, Slobodan Ilic
We address the problem of 3D pose estimation of multiple humans from multiple views.
Ranked #15 on 3D Multi-Person Pose Estimation on Campus
no code implementations • 12 Nov 2020 • Andreas Doering, Di Chen, Shanshan Zhang, Bernt Schiele, Juergen Gall
For that reason, we present PoseTrackReID, a large-scale dataset for multi-person pose tracking and video-based person re-ID.
no code implementations • 1 Feb 2021 • Keyang Zhou, Bharat Lal Bhatnagar, Bernt Schiele, Gerard Pons-Moll
The remarkable result is that with only self-supervision, ART facilitates learning a unique canonical orientation for both rigid and nonrigid shapes, which leads to a notable boost in performance of aforementioned tasks.
1 code implementation • NeurIPS 2020 • Jiangxin Dong, Stefan Roth, Bernt Schiele
We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning.
no code implementations • ICCV 2021 • David Stutz, Matthias Hein, Bernt Schiele
To this end, we propose average- and worst-case metrics to measure flatness in the robust loss landscape and show a correlation between good robust generalization and flatness.
no code implementations • 16 Apr 2021 • David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele
Moreover, we present a novel adversarial bit error attack and are able to obtain robustness against both targeted and untargeted bit-level attacks.
no code implementations • CONLL 2020 • Xudong Hong, Rakshith Shetty, Asad Sayeed, Khushboo Mehra, Vera Demberg, Bernt Schiele
A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text.
Ranked #5 on Visual Storytelling on VIST
no code implementations • CVPR 2021 • Apratim Bhattacharyya, Daniel Olmeda Reino, Mario Fritz, Bernt Schiele
In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories.
Ranked #1 on Pedestrian Trajectory Prediction on Euro-PVI
no code implementations • CVPR 2021 • Jiangxin Dong, Stefan Roth, Bernt Schiele
The classical maximum a-posteriori (MAP) framework for non-blind image deblurring requires defining suitable data and regularization terms, whose interplay yields the desired clear image through optimization.
no code implementations • 29 Sep 2021 • Yaoyao Liu, Bernt Schiele, Qianru Sun
However, we empirically observe that this both harms learning of new classes and also underperforms to distil old class knowledge from the previous phase model.
no code implementations • 25 Sep 2019 • Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success.
no code implementations • 25 Sep 2019 • David Stutz, Matthias Hein, Bernt Schiele
Adversarial training is the standard to train models robust against adversarial examples.
no code implementations • 10 Dec 2021 • Yue Fan, Anna Kukleva, Bernt Schiele
Generally, the aim is to train a model that is invariant to various data augmentations.
no code implementations • 4 Apr 2022 • Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata
While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features.
Ranked #5 on GZSL Video Classification on ActivityNet-GZSL(main)
no code implementations • 26 Apr 2022 • Nils Philipp Walter, David Stutz, Bernt Schiele
In order to shed light on the role of BN in adversarial training, we investigate to what extent the expressiveness of BN can be used to robustify fragile features in comparison to random features.
no code implementations • 12 May 2022 • Yue Zhao, Yantao Shen, Yuanjun Xiong, Shuo Yang, Wei Xia, Zhuowen Tu, Bernt Schiele, Stefano Soatto
We present a method to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model.
no code implementations • 13 Sep 2022 • Achin Jain, Kibok Lee, Gurumurthy Swaminathan, Hao Yang, Bernt Schiele, Avinash Ravichandran, Onkar Dabeer
Combined with a matching loss, it can effectively find objects that are similar to the input patch and complete the missing annotations.
no code implementations • 23 Sep 2022 • Samarth Sinha, Peter Gehler, Francesco Locatello, Bernt Schiele
We find that TeST sets the new state-of-the art for test-time domain adaptation algorithms.