Search Results for author:
Found 8 papers, 5 papers with code
SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow
Increasing the annotation efficiency of trajectory annotations from videos has the potential to enable the next generation of data-hungry tracking algorithms to thrive on large-scale datasets.
Unifying Short and Long-Term Tracking with Graph Hierarchies
Tracking objects over long videos effectively means solving a spectrum of problems, from short-term association for un-occluded objects to long-term association for objects that are occluded and then reappear in the scene.
Ranked #2 on
Multiple Object Tracking
on BDD100K test
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes
This allows us to improve the generalization of a CNN-based model without any increase in the number of weights at test time.
PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?
This allows our graph neural network to learn to effectively encode temporal and spatial interactions and fully leverage contextual and motion cues to obtain final scene interpretation by posing data association as edge classification.
DeVIS: Making Deformable Transformers Work for Video Instance Segmentation
To reason about all VIS subtasks jointly over multiple frames, we present temporal multi-scale deformable attention with instance-aware object queries.
Ranked #15 on
Video Instance Segmentation
on YouTube-VIS 2021
Simple Cues Lead to a Strong Multi-Object Tracker
For association, most models resourced to motion and appearance cues, e. g., re-identification networks.
The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation
We introduce CenterGroup, an attention-based framework to estimate human poses from a set of identity-agnostic keypoints and person center predictions in an image.
Ranked #6 on
Multi-Person Pose Estimation
on MS COCO
Learning a Neural Solver for Multiple Object Tracking
Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-detection paradigm.
