2018
Video Based Reconstruction of 3D People Models
T. Alldieck, M. A. Magnor, W. Xu, C. Theobalt and G. Pons-Moll
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
M. Andriluka, U. Iqbal, A. Milan, E. Insafutdinov, L. Pishchulin, J. Gall and B. Schiele
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Accurate and Diverse Sampling of Sequences based on a “Best of Many” Sample Objective
A. Bhattacharyya, M. Fritz and B. Schiele
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty
A. Bhattacharyya, M. Fritz and B. Schiele
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Disentangled Person Image Generation
L. Ma, Q. Sun, S. Georgoulis, L. Van Gool, B. Schiele and M. Fritz
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
T. Orekondy, M. Fritz and B. Schiele
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
D. H. Park, L. A. Hendricks, Z. Akata, A. Rohrbach, B. Schiele, T. Darrell and M. Rohrbach
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Natural and Effective Obfuscation by Head Inpainting
Q. Sun, L. Ma, S. J. Oh, L. Van Gool, B. Schiele and M. Fritz
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Feature Generating Networks for Zero-Shot Learning
Y. Xian, T. Lorenz, B. Schiele and Z. Akata
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor
T. Yu, Z. Zheng, K. Guo, J. Zhao, Q. Dai, H. Li, G. Pons-Moll and Y. Liu
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Occluded Pedestrian Detection through Guided Attention in CNNs
S. Zhang, J. Yang and B. Schiele
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Quick Bootstrapping of a Personalized Gaze Model from Real-Use Interactions
M. X. Huang, J. Li, G. Ngai and H. Va Leong
ACM Transactions on Intelligent Systems and Technology, Volume 9, Number 4, 2018
VRPursuits: Interaction in Virtual Reality using Smooth Pursuit Eye Movements
M. Khamis, C. Oechsner, F. Alt and A. Bulling
AVI’18, International Conference on Advanced Visual Interfaces, 2018
(Accepted/in press)
Understanding Face and Eye Visibility in Front-Facing Cameras of Smartphones used in the Wild
M. Khamis, A. Baier, N. Henze, F. Alt and A. Bulling
CHI 2018, CHI Conference on Human Factors in Computing Systems, 2018
Which one is me? Identifying Oneself on Public Displays
M. Khamis, C. Becker, A. Bulling and F. Alt
CHI 2018, CHI Conference on Human Factors in Computing Systems, 2018
Training Person-Specific Gaze Estimators from Interactions with Multiple Devices
X. Zhang, M. X. Huang, Y. Sugano and A. Bulling
CHI 2018, CHI Conference on Human Factors in Computing Systems, 2018
GazeDirector: Fully Articulated Eye Gaze Redirection in Video
E. Wood, T. Baltrusaitis, L.-P. Morency, P. Robinson and A. Bulling
Computer Graphics Forum (Proc. EUROGRAPHICS 2018), Volume 37, Number 2, 2018
GazeDrone: Mobile Eye-Based Interaction in Public Space Without Augmenting the User
M. Khamis, A. Kienle, F. Alt and A. Bulling
DroNet’18, 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, 2018
A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks
A. Khan, I. Steiner, Y. Sugano, A. Bulling and R. Macdonald
Eleventh International Language Resources and Evaluation Conference (LREC 2018), 2018
(Accepted/in press)
Eye Movements During Everyday Behavior Predict Personality Traits
S. Hoppe, T. Loetscher, S. Morey and A. Bulling
Frontiers in Human Neuroscience, Volume 12, 2018
Image and Video Captioning with Augmented Neural Architectures
R. Shetty, H. R. Tavakoli and J. Laaksonen
IEEE MultiMedia, Volume Early Access, 2018
Fast-PADMA: Rapidly Adapting Facial Affect Model from Similar Individuals
M. X. Huang, J. Li, G. Ngai, H. V. Leong and K. A. Hua
IEEE Transactions on Multimedia, Volume Early Access, 2018
Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification
M. Lapin, M. Hein and B. Schiele
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 40, Number 7, 2018
Towards Reaching Human Performance in Pedestrian Detection
S. Zhang, R. Benenson, M. Omran, J. Hosang and B. Schiele
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 40, Number 4, 2018
Abstract
Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the “perfect single frame detector”. We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech pedestrian dataset). After manually clustering the frequent errors of a top detector, we characterise both localisation and background- versus-foreground errors. To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can improve results even with a small portion of sanitised training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech pedestrian dataset, and provide a new sanitised set of training and test annotations.
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
X. Zhang, Y. Sugano, M. Fritz and A. Bulling
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume Early Access, 2018
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
S. Yeung, O. Russakovsky, N. Jin, M. Andriluka, G. Mori and L. Fei-Fei
International Journal of Computer Vision, Volume 126, Number 2-4, 2018
Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour
P. Müller, M. X. Huang and A. Bulling
IUI 2018, 23rd International Conference on Intelligent User Interfaces, 2018
The Past, Present, and Future of Gaze-enabled Handheld Mobile Devices: Survey and Lessons Learned
M. Khamis, F. Alt and A. Bulling
MobileHCI’18, 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, 2018
(Accepted/in press)
Error-Aware Gaze-Based Interfaces for Robust Mobile Gaze Interaction
M. Barz, F. Daiber, D. Sonntag and A. Bulling
Proceedings ETRA 2018, 2018
(Accepted/in press)
A Novel Approach to Single Camera, Glint-Free 3D Eye Model Fitting Including Corneal Refraction
K. Dierkes, M. Kassner and A. Bulling
Proceedings ETRA 2018, 2018
(Accepted/in press)
Hidden Pursuits: Evaluating Gaze-selection via Pursuits when the Stimulus Trajectory is Partially Hidden
T. Mattusch, M. Mirzamohammad, M. Khamis, A. Bulling and F. Alt
Proceedings ETRA 2018, 2018
(Accepted/in press)
Robust Eye Contact Detection in Natural Multi-Person Interactions Using Gaze and Speaking Behaviour
P. Müller, M. X. Huang, X. Zhang and A. Bulling
Proceedings ETRA 2018, 2018
(Accepted/in press)
Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings
S. Park, X. Zhang, A. Bulling and O. Hilliges
Proceedings ETRA 2018, 2018
(Accepted/in press)
Fixation Detection for Head-Mounted Eye Tracking Based on Visual Similarity of Gaze Targets
J. Steil, M. X. Huang and A. Bulling
Proceedings ETRA 2018, 2018
(Accepted/in press)
Revisiting Data Normalization for Appearance-Based Gaze Estimation
X. Zhang, Y. Sugano and A. Bulling
Proceedings ETRA 2018, 2018
(Accepted/in press)
Advanced Steel Microstructure Classification by Deep Learning Methods
S. M. Azimi, D. Britz, M. Engstler, M. Fritz and F. Mücklich
Scientific Reports, Volume 8, 2018
Abstract
The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which opens doors for huge uncertainties. Since the microstructure could be a combination of different phases with complex substructures its automatic classification is very challenging and just a little work in this field has been carried out. Prior related works apply mostly designed and engineered features by experts and classify microstructure separately from feature extraction step. Recently Deep Learning methods have shown surprisingly good performance in vision applications by learning the features from data together with the classification step. In this work, we propose a deep learning method for microstructure classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy, indicating the effectiveness of pixel-wise approaches. Beyond the success presented in this paper, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.
Towards Reverse-Engineering Black-Box Neural Networks
S. J. Oh, M. Augustin, B. Schiele and M. Fritz
Sixth International Conference on Learning Representations (ICLR 2018), 2018
(Accepted/in press)
Long-Term Image Boundary Prediction
A. Bhattacharyya, M. Malinowski, B. Schiele and M. Fritz
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
Learning to Refine Human Pose Estimation
M. Fieraru, A. Khoreva, L. Pishchulin and B. Schiele
Technical Report, 2018
(arXiv: 1804.07909)
Abstract
Multi-person pose estimation in images and videos is an important yet challenging task with many applications. Despite the large improvements in human pose estimation enabled by the development of convolutional neural networks, there still exist a lot of difficult cases where even the state-of-the-art models fail to correctly localize all body joints. This motivates the need for an additional refinement step that addresses these challenging cases and can be easily applied on top of any existing method. In this work, we introduce a pose refinement network (PoseRefiner) which takes as input both the image and a given pose estimate and learns to directly predict a refined pose by jointly reasoning about the input-output space. In order for the network to learn to refine incorrect body joint predictions, we employ a novel data augmentation scheme for training, where we model "hard" human pose cases. We evaluate our approach on four popular large-scale pose estimation benchmarks such as MPII Single- and Multi-Person Pose Estimation, PoseTrack Pose Estimation, and PoseTrack Pose Tracking, and report systematic improvement over the state of the art.
Video Object Segmentation with Language Referring Expressions
A. Khoreva, A. Rohrbach and B. Schiele
Technical Report, 2018
(arXiv: 1803.08006)
Abstract
Most state-of-the-art semi-supervised video object segmentation methods rely on a pixel-accurate mask of a target object provided for the first frame of a video. However, obtaining a detailed segmentation mask is expensive and time-consuming. In this work we explore an alternative way of identifying a target object, namely by employing language referring expressions. Besides being a more practical and natural way of pointing out a target object, using language specifications can help to avoid drift as well as make the system more robust to complex dynamics and appearance variations. Leveraging recent advances of language grounding models designed for images, we propose an approach to extend them to video data, ensuring temporally coherent predictions. To evaluate our method we augment the popular video object segmentation benchmarks, DAVIS'16 and DAVIS'17 with language descriptions of target objects. We show that our 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.
From Perception over Anticipation to Manipulation
W. Li
PhD Thesis, Universität des Saarlandes, 2018
Abstract
From autonomous driving cars to surgical robots, robotic system has enjoyed significant growth over the past decade. With the rapid development in robotics alongside the evolution in the related fields, such as computer vision and machine learning, integrating perception, anticipation and manipulation is key to the success of future robotic system. In this thesis, we explore different ways of such integration to extend the capabilities of a robotic system to take on more challenging real world tasks. On anticipation and perception, we address the recognition of ongoing activity from videos. In particular we focus on long-duration and complex activities and hence propose a new challenging dataset to facilitate the work. We introduce hierarchical labels over the activity classes and investigate the temporal accuracy-specificity trade-offs. We propose a new method based on recurrent neural networks that learns to predict over this hierarchy and realize accuracy specificity trade-offs. Our method outperforms several baselines on this new challenge. On manipulation with perception, we propose an efficient framework for programming a robot to use human tools. We first present a novel and compact model for using tools described by a tip model. Then we explore a strategy of utilizing a dual-gripper approach for manipulating tools – motivated by the absence of dexterous hands on widely available general purpose robots. Afterwards, we embed the tool use learning into a hierarchical architecture and evaluate it on a Baxter research robot. Finally, combining perception, anticipation and manipulation, we focus on a block stacking task. First we explore how to guide robot to place a single block into the scene without collapsing the existing structure. We introduce a mechanism to predict physical stability directly from visual input and evaluate it first on a synthetic data and then on real-world block stacking. Further, we introduce the target stacking task where the agent stacks blocks to reproduce a tower shown in an image. To do so, we create a synthetic block stacking environment with physics simulation in which the agent can learn block stacking end-to-end through trial and error, bypassing to explicitly model the corresponding physics knowledge. We propose a goal-parametrized GDQN model to plan with respect to the specific goal. We validate the model on both a navigation task in a classic gridworld environment and the block stacking task.
Deep Appearance Maps
M. Maximov, T. Ritschel and M. Fritz
Technical Report, 2018
(arXiv: 1804.00863)
Abstract
We propose a deep representation of appearance, i. e. the relation of color, surface orientation, viewer position, material and illumination. Previous approaches have used deep learning to extract classic appearance representations relating to reflectance model parameters (e. g. Phong) or illumination (e. g. HDR environment maps). We suggest to directly represent appearance itself as a network we call a deep appearance map (DAM). This is a 4D generalization over 2D reflectance maps, which held the view direction fixed. First, we show how a DAM can be learned from images or video frames and later be used to synthesize appearance, given new surface orientations and viewer positions. Second, we demonstrate how another network can be used to map from an image or video frames to a DAM network to reproduce this appearance, without using a lengthy optimization such as stochastic gradient descent (learning-to-learn). Finally, we generalize this to an appearance estimation-and-segmentation task, where we map from an image showing multiple materials to multiple networks reproducing their appearance, as well as per-pixel segmentation.
Understanding and Controlling User Linkability in Decentralized Learning
T. Orekondy, S. J. Oh, B. Schiele and M. Fritz
Technical Report, 2018
(arXiv: 1805.05838)
Abstract
Machine Learning techniques are widely used by online services (e.g. Google, Apple) in order to analyze and make predictions on user data. As many of the provided services are user-centric (e.g. personal photo collections, speech recognition, personal assistance), user data generated on personal devices is key to provide the service. In order to protect the data and the privacy of the user, federated learning techniques have been proposed where the data never leaves the user's device and "only" model updates are communicated back to the server. In our work, we propose a new threat model that is not concerned with learning about the content - but rather is concerned with the linkability of users during such decentralized learning scenarios. We show that model updates are characteristic for users and therefore lend themselves to linkability attacks. We show identification and matching of users across devices in closed and open world scenarios. In our experiments, we find our attacks to be highly effective, achieving 20x-175x chance-level performance. In order to mitigate the risks of linkability attacks, we study various strategies. As adding random noise does not offer convincing operation points, we propose strategies based on using calibrated domain-specific data; we find these strategies offers substantial protection against linkability threats with little effect to utility.
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
R. Shetty, M. Fritz and B. Schiele
Technical Report, 2018
(arXiv: 1806.01911)
Abstract
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. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to find and remove objects from general scene images using image-level labels and unpaired data in a generative adversarial network (GAN) framework. We achieve this with two key contributions: a two-stage editor architecture consisting of a mask generator and image in-painter that co-operate to remove objects, and a novel GAN based prior for the mask generator that allows us to flexibly incorporate knowledge about object shapes. We experimentally show on two datasets that our method effectively removes a wide variety of objects using weak supervision only
PrivacEye: Privacy-Preserving First-Person Vision Using Image Features and Eye Movement Analysis
J. Steil, M. Koelle, W. Heuten, S. Boll and A. Bulling
Technical Report, 2018
(arXiv: 1801.04457)
Abstract
As first-person cameras in head-mounted displays become increasingly prevalent, so does the problem of infringing user and bystander privacy. To address this challenge, we present PrivacEye, a proof-of-concept system that detects privacysensitive everyday situations and automatically enables and disables the first-person camera using a mechanical shutter. To close the shutter, PrivacEye detects sensitive situations from first-person camera videos using an end-to-end deep-learning model. To open the shutter without visual input, PrivacEye uses a separate, smaller eye camera to detect changes in users' eye movements to gauge changes in the "privacy level" of the current situation. We evaluate PrivacEye on a dataset of first-person videos recorded in the daily life of 17 participants that they annotated with privacy sensitivity levels. We discuss the strengths and weaknesses of our proof-of-concept system based on a quantitative technical evaluation as well as qualitative insights from semi-structured interviews.
Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors
J. Steil, P. Müller, Y. Sugano and A. Bulling
Technical Report, 2018
(arXiv: 1801.06011)
Abstract
Users' visual attention is highly fragmented during mobile interactions but the erratic nature of these attention shifts currently limits attentive user interfaces to adapt after the fact, i.e. after shifts have already happened, thereby severely limiting the adaptation capabilities and user experience. To address these limitations, we study attention forecasting -- the challenging task of predicting whether users' overt visual attention (gaze) will shift between a mobile device and environment in the near future or how long users' attention will stay in a given location. To facilitate the development and evaluation of methods for attention forecasting, we present a novel long-term dataset of everyday mobile phone interactions, continuously recorded from 20 participants engaged in common activities on a university campus over 4.5 hours each (more than 90 hours in total). As a first step towards a fully-fledged attention forecasting interface, we further propose a proof-of-concept method that uses device-integrated sensors and body-worn cameras to encode rich information on device usage and users' visual scene. We demonstrate the feasibility of forecasting bidirectional attention shifts between the device and the environment as well as for predicting the first and total attention span on the device and environment using our method. We further study the impact of different sensors and feature sets on performance and discuss the significant potential but also remaining challenges of forecasting user attention during mobile interactions.
A Hybrid Model for Identity Obfuscation by Face Replacement
Q. Sun, A. Tewari, W. Xu, M. Fritz, C. Theobalt and B. Schiele
Technical Report, 2018
(arXiv: 1804.04779)
Abstract
As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis. On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity. On the other hand, the data-driven aspects allow for adding fine details and overall realism as well as seamless blending into the scene context. In our experiments, we show highly realistic output of our system that improves over the previous state of the art in obfuscation rate while preserving a higher similarity to the original image content.