Search Results for author:
Found 8 papers, 3 papers with code
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
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)
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications
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.
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
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.
The Cityscapes Dataset for Semantic Urban Scene Understanding
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications.
How Far are We from Solving Pedestrian Detection?
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.
Weakly Supervised Object Boundaries
State-of-the-art learning based boundary detection methods require extensive training data.
Ranked #2 on
Edge Detection
on SBD
Taking a Deeper Look at Pedestrians
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
Ten Years of Pedestrian Detection, What Have We Learned?
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.
