2021
Learning Decision Trees Recurrently Through Communication
S. Alaniz, D. Marcos, B. Schiele and Z. Akata
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers
A. Bhattacharyya, D. O. Reino, M. Fritz and B. Schiele
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Convolutional Dynamic Alignment Networks for Interpretable Classifications
M. D. Böhle, M. Fritz and B. Schiele
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Distilling Audio-Visual Knowledge by Compositional Contrastive Learning
Y. Chen, Y. Xian, A. S. Koepke and Z. Akata
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Stereo Radiance Fields (SRF): Learning View Synthesis from Sparse Views of Novel Scenes
J. Chibane, A. Bansal, V. Lazova and G. Pons-Moll
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Learning Spatially-Variant MAP Models for Non-blind Image Deblurring
J. Dong, S. Roth and B. Schiele
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Adaptive Aggregation Networks for Class-Incremental Learning
Y. Liu, B. Schiele and Q. Sun
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Open World Compositional Zero-Shot Learning
M. Mancini, M. F. Naeem, Y. Xian and Z. Akata
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Learning Graph Embeddings for Compositional Zero-shot Learning
M. F. Naeem, Y. Xian, F. Tombari and Z. Akata
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
SMPLicit: Topology-aware Generative Model for Clothed People
G. Pons-Moll, F. Moreno-Noguer, E. Corona, A. Pumarola and G. Alenyà
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
D-NeRF: Neural Radiance Fields for Dynamic Scenes
A. Pumarola, E. Corona, G. Pons-Moll and F. Moreno-Noguer
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs
H.-P. Wang, N. Yu and M. Fritz
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
Deep Outlier Handling for Image Deblurring
J. Dong and J. Pan
IEEE Transactions on Image Processing, Volume 30, 2021
Future Moment Assessment for Action Query
Q. Ke, M. Fritz and B. Schiele
IEEE Winter Conference on Applications of Computer Vision (WACV 2021), 2021
Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences
R. G. VidalMata, W. J. Scheirer, A. Kukleva, D. Cox and H. Kuehne
IEEE Winter Conference on Applications of Computer Vision (WACV 2021), 2021
Guest Editorial: Special Issue on “Computer Vision for All Seasons: Adverse Weather and Lighting Conditions”
D. Dai, R. T. Tan, V. Patel, J. Matas, B. Schiele and L. Van Gool
International Journal of Computer Vision, 2021
Guided Attention in CNNs for Occluded Pedestrian Detection and Re-identification
S. Zhang, D. Chen, J. Yang and B. Schiele
International Journal of Computer Vision, 2021
Bit Error Robustness for Energy-Efficient DNN Accelerators
D. Stutz, N. Chandramoorthy, M. Hein and B. Schiele
Proceedings of the 4th MLSys Conference, 2021
Abstract
Deep neural network (DNN) accelerators received considerable attention in past years due to saved energy compared to mainstream hardware. 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. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, and random bit error training (RandBET) improves robustness against random bit errors in (quantized) DNN weights significantly. This leads to high energy savings from both low-voltage operation as well as low-precision quantization. Our approach generalizes across operating voltages and accelerators, as demonstrated on bit errors from profiled SRAM arrays. We also discuss why weight clipping alone is already a quite effective way to achieve robustness against bit errors. Moreover, we specifically discuss the involved trade-offs regarding accuracy, robustness and precision: Without losing more than 1% in accuracy compared to a normally trained 8-bit DNN, we can reduce energy consumption on CIFAR-10 by 20%. Higher energy savings of, e.g., 30%, are possible at the cost of 2.5% accuracy, even for 4-bit DNNs.
Spectral Distribution Aware Image Generation
S. Jung and M. Keuper
Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
Implicit Feature Networks for Texture Completion from Partial 3D Data
J. Chibane and G. Pons-Moll
Computer Vision -- ECCV Workshops 2020, 2020
Adversarial Training Against Location-Optimized Adversarial Patches
S. Rao, D. Stutz and B. Schiele
Computer Vision -- ECCV Workshops 2020, 2020
Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction
H. Sattar, K. Krombholz, G. Pons-Moll and M. Fritz
Computer Vision -- ECCV Workshops 2020, 2020
Abstract
Modern approaches to pose and body shape estimation have recently achieved strong performance even under challenging real-world conditions. Even from a single image of a clothed person, a realistic looking body shape can be inferred that captures a users' weight group and body shape type well. This opens up a whole spectrum of applications -- in particular in fashion -- where virtual try-on and recommendation systems can make use of these new and automatized cues. However, a realistic depiction of the undressed body is regarded highly private and therefore might not be consented by most people. Hence, we ask if the automatic extraction of such information can be effectively evaded. While adversarial perturbations have been shown to be effective for manipulating the output of machine learning models -- in particular, end-to-end deep learning approaches -- state of the art shape estimation methods are composed of multiple stages. We perform the first investigation of different strategies that can be used to effectively manipulate the automatic shape estimation while preserving the overall appearance of the original image.
Haar Wavelet based Block Autoregressive Flows for Trajectories
A. Bhattacharyya, C.-N. Straehle, M. Fritz and B. Schiele
Pattern Recognition (GCPR 2020), 2020
Analyzing the Dependency of ConvNets on Spatial Information
Y. Fan, Y. Xian, M. M. Losch and B. Schiele
Pattern Recognition (GCPR 2020), 2020
Long-Term Anticipation of Activities with Cycle Consistency
Y. A. Farha, Q. Ke, B. Schiele and J. Gall
Pattern Recognition (GCPR 2020), 2020
On the Lifted Multicut Polytope for Trees
J.-H. Lange and B. Andres
Pattern Recognition (GCPR 2020), 2020
Semantic Bottlenecks: Quantifying & Improving Inspectability of Deep Representations
M. Losch, M. Fritz and B. Schiele
Pattern Recognition (GCPR 2020), 2020
Long-Tailed Recognition Using Class-Balanced Experts
S. Sharma, N. Yu, M. Fritz and B. Schiele
Pattern Recognition (GCPR 2020), 2020