News
[March 2022] |
We are organizing the BMTT workshop at CVPR 2022. Check out our synth2real chalenges for tracking! |
[March 2022] |
Just created this website! |
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Research
I have a general interest in Machine Learning and Computer Vision. Some of the tasks I focus on are detection, segmentation, tracking and human pose estimation. I am also broadly interested in leveraging ideas from classical graph-based approaches and optimization in combination with deep learning to solve vision problems.
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The Center of Attention: Center-Keypoint Attention for Multi-Person Pose Estimation
Guillem Brasó, Nikita Kister, Laura Leal-Taixé
ICCV 2021
We propose a multi-head attention-based framework for end-to-end bottom-up multi-person pose estimation. Our main idea is to detect both keypoints and object centers, and use cross-attention among them to group keypoints into human poses.
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MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?
Matteo Fabbri, Guillem Brasó,Gianluca Maugeri, Orcun Cetintas, Riccardo Gasparini, Aljosa Osep, Simone Calderara, Laura Leal-Taixe, Rita Cucchiara
ICCV 2021
We introduce MOTSynth, a large-scale synthetic dataset for pedestrian tracking, detection, segmentation and 2D/3D keypoint estimation. We show that trackers and detectors trained on our dataset achieve impressive generalization results when tested on real data.
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Learning a Neural Solver for Multiple Object Tracking
Guillem Brasó, Laura Leal-Taixé
CVPR 2020   (Oral Presentation)
Inspired by classical graph-based multi-object tracking methods, we propose a neural message passing framework for data association in multi-object tracking. We present the first graph-structured deep learning module for tracking, and achieve impressive results on the MOTChallenge benchmarks.
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