activity
20222026
most citedSPAMming Labels: Efficient Annotations for the Trackers of Tomorrow

5 citations · 11 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Foundation4D: Towards a unified perception and reconstruction model for autonomous vehicles

NVIDIA, :, Sérgio Agostinho +25

We introduce Foundation4D, a unified feed-forward model for geometry estimation, 3D detection, semantic occupancy, and Gaussian reconstruction from multi-camera driving video. A sh…

cs.CV2026

Beyond the Image Plane: World-Grounded Queries for Multi-Object Tracking

Orcun Cetintas, Guillem Brasó, Tim Meinhardt +1

Monocular videos record 3D scenes as sequences of 2D image-plane projections, obscuring depth and spatial relationships. Multi-object trackers localize and associate objects primar…

cs.CV2025

NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking

Benjamin Missaoui, Orcun Cetintas, Guillem Brasó +2

The long-standing division between \textit{online} and \textit{offline} Multi-Object Tracking (MOT) has led to fragmented solutions that fail to address the flexible temporal requi…

cs.CV2025

Native Segmentation Vision Transformers

Guillem Brasó, Aljoša Ošep, Laura Leal-Taixé

Uniform downsampling remains the de facto standard for reducing spatial resolution in vision backbones. In this work, we propose an alternative design built around a content-aware…

cs.CV2024★ 5 cited

SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow

Orcun Cetintas, Tim Meinhardt, Guillem Brasó +1

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-sca…

cs.CV2022★ 3 cited

The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes

Peter Kocsis, Peter Súkeník, Guillem Brasó +3

Convolutional neural networks were the standard for solving many computer vision tasks until recently, when Transformers of MLP-based architectures have started to show competitive…

cs.CV2022★ 3 cited

DeVIS: Making Deformable Transformers Work for Video Instance Segmentation

Adrià Caelles, Tim Meinhardt, Guillem Brasó +1

Video Instance Segmentation (VIS) jointly tackles multi-object detection, tracking, and segmentation in video sequences. In the past, VIS methods mirrored the fragmentation of thes…