226 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Toyota Research Institute8 papers
- Massachusetts Institute of TechnologyUS5 papers
- Toyota Central Research and Development Laboratories (Japan)JP5 papers
- Keio UniversityJP4 papers
- University of California, BerkeleyUS4 papers
- University of MichiganUS3 papers
- Ann Arbor Center for Independent LivingUS2 papers
- Columbia UniversityUS2 papers
- Georgia State UniversityUS2 papers
- IBM Research - TokyoJP2 papers
- Infotech Soft (United States)US2 papers
- Institut national de recherche en sciences et technologies du numériqueFR2 papers
17 papers · 1 filter
ViA: View-invariant Skeleton Action Representation Learning via Motion Retargeting
Di Yang, Yaohui Wang, Antitza Dantcheva +3
Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory setti…
The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video
John Gideon, Simon Stent
The ability to reliably estimate physiological signals from video is a powerful tool in low-cost, pre-clinical health monitoring. In this work we propose a new approach to remote p…
Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
Farzaneh Rezaeianaran, Rakshith Shetty, Rahaf Aljundi +3
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…
Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency
Aditya Ganeshan, Alexis Vallet, Yasunori Kudo +5
Deep learning models for semantic segmentation rely on expensive, large-scale, manually annotated datasets. Labelling is a tedious process that can take hours per image. Automatica…
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers
Apratim Bhattacharyya, Daniel Olmeda Reino, Mario Fritz +1
Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle a…
MarioNette: Self-Supervised Sprite Learning
Dmitriy Smirnov, Michael Gharbi, Matthew Fisher +3
Artists and video game designers often construct 2D animations using libraries of sprites -- textured patches of objects and characters. We propose a deep learning approach that de…
Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos
Di Yang, Rui Dai, Yaohui Wang +4
Taking advantage of human pose data for understanding human activities has attracted much attention these days. However, state-of-the-art pose estimators struggle in obtaining high…
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
Vitor Guizilini, Rui Hou, Jie Li +2
Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning re…
Neural Outlier Rejection for Self-Supervised Keypoint Learning
Jiexiong Tang, Hanme Kim, Vitor Guizilini +2
Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms. Recently, several learned keypoint methods have demonstra…
Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core
Wadim Kehl, Federico Tombari, Slobodan Ilic +1
We present a novel method to track 3D models in color and depth data. To this end, we introduce approximations that accelerate the state-of-the-art in region-based tracking by an o…
Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors
Sergey Zakharov, Wadim Kehl, Arjun Bhargava +1
We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data. Our autolabeling method solves an…
SPIGAN: Privileged Adversarial Learning from Simulation
Kuan-Hui Lee, German Ros, Jie Li +1
Deep Learning for Computer Vision depends mainly on the source of supervision.Photo-realistic simulators can generate large-scale automatically labeled syntheticdata, but introduce…
Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks
Adrià Recasens, Petr Kellnhofer, Simon Stent +2
We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. Our differentiable layer…
Deep-Temporal LSTM for Daily Living Action Recognition
Srijan Das, Michal Koperski, Francois Bremond +1
In this paper, we propose to improve the traditional use of RNNs by employing a many to many model for video classification. We analyze the importance of modeling spatial layout an…
Error Correction for Dense Semantic Image Labeling
Yu-Hui Huang, Xu Jia, Stamatios Georgoulis +2
Pixelwise semantic image labeling is an important, yet challenging, task with many applications. Typical approaches to tackle this problem involve either the training of deep netwo…
Natural and Effective Obfuscation by Head Inpainting
Qianru Sun, Liqian Ma, Seong Joon Oh +3
As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted t…
Neural Self Talk: Image Understanding via Continuous Questioning and Answering
Yezhou Yang, Yi Li, Cornelia Fermuller +1
In this paper we consider the problem of continuously discovering image contents by actively asking image based questions and subsequently answering the questions being asked. The…