288 citations · 351 across the 18 of their papers we have counts for
6 papers · 1 filter
Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking
Mattia Segu, Luigi Piccinelli, Siyuan Li +3
Multiple object tracking in complex scenarios - such as coordinated dance performances, team sports, or dynamic animal groups - presents unique challenges. In these settings, objec…
Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs
Mattia Segu, Luigi Piccinelli, Siyuan Li +3
The supervision of state-of-the-art multiple object tracking (MOT) methods requires enormous annotation efforts to provide bounding boxes for all frames of all videos, and instance…
SLAck: Semantic, Location, and Appearance Aware Open-Vocabulary Tracking
Siyuan Li, Lei Ke, Yung-Hsu Yang +4
Open-vocabulary Multiple Object Tracking (MOT) aims to generalize trackers to novel categories not in the training set. Currently, the best-performing methods are mainly based on p…
Matching Anything by Segmenting Anything
Siyuan Li, Lei Ke, Martin Danelljan +4
The robust association of the same objects across video frames in complex scenes is crucial for many applications, especially Multiple Object Tracking (MOT). Current methods predom…
Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning
Rui Li, Tobias Fischer, Mattia Segu +3
Recovering the 3D scene geometry from a single view is a fundamental yet ill-posed problem in computer vision. While classical depth estimation methods infer only a 2.5D scene repr…
UniDepth: Universal Monocular Metric Depth Estimation
Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis +4
Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is c…