Search Results for author: Laura Leal-Taixe

Found 27 papers, 18 papers with code

Multi-Object Tracking and Segmentation via Neural Message Passing

1 code implementation15 Jul 2022 Guillem Braso, Orcun Cetintas, Laura Leal-Taixe

We achieve state-of-the-art results for both tracking and segmentation in several publicly available datasets.

Association Multi-Object Tracking +2

The Group Loss++: A deeper look into group loss for deep metric learning

no code implementations4 Apr 2022 Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixe

Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.

Image Retrieval Metric Learning +2

Text2Pos: Text-to-Point-Cloud Cross-Modal Localization

no code implementations CVPR 2022 Manuel Kolmet, Qunjie Zhou, Aljosa Osep, Laura Leal-Taixe

Natural language-based communication with mobile devices and home appliances is becoming increasingly popular and has the potential to become natural for communicating with mobile robots in the future.

DeepLab2: A TensorFlow Library for Deep Labeling

1 code implementation17 Jun 2021 Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, Daniel Cremers, Laura Leal-Taixe, Alan L. Yuille, Florian Schroff, Hartwig Adam, Liang-Chieh Chen

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.

TrackFormer: Multi-Object Tracking with Transformers

2 code implementations CVPR 2022 Tim Meinhardt, Alexander Kirillov, Laura Leal-Taixe, Christoph Feichtenhofer

The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories.

Ranked #4 on Multi-Object Tracking on MOTS20 (using extra training data)

Association Multi-Object Tracking +1

Patch2Pix: Epipolar-Guided Pixel-Level Correspondences

1 code implementation CVPR 2021 Qunjie Zhou, Torsten Sattler, Laura Leal-Taixe

In this work, we propose a new perspective to estimate correspondences in a detect-to-refine manner, where we first predict patch-level match proposals and then refine them.

Homography Estimation Visual Localization

Photi-LakeIce Dataset

1 code implementation ISPRS Congress 2020 Rajanie Prabha, Manu Tom, Mathias Rothermel, Emmanuel Baltsavias, Laura Leal-Taixe, Konrad Schindler

On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.

Change detection for remote sensing images Image Segmentation +4

Lake Ice Monitoring with Webcams and Crowd-Sourced Images

2 code implementations18 Feb 2020 Rajanie Prabha, Manu Tom, Mathias Rothermel, Emmanuel Baltsavias, Laura Leal-Taixe, Konrad Schindler

On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.

Change detection for remote sensing images Image Segmentation +4

The Group Loss for Deep Metric Learning

2 code implementations ECCV 2020 Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixe

Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.

Ranked #17 on Metric Learning on CUB-200-2011 (using extra training data)

Image Retrieval Metric Learning +1

To Learn or Not to Learn: Visual Localization from Essential Matrices

1 code implementation4 Aug 2019 Qunjie Zhou, Torsten Sattler, Marc Pollefeys, Laura Leal-Taixe

Using a classical feature-based approach within this framework, we show state-of-the-art performance.

Mixed Reality Pose Estimation +2

How To Train Your Deep Multi-Object Tracker

2 code implementations CVPR 2020 Yihong Xu, Aljosa Osep, Yutong Ban, Radu Horaud, Laura Leal-Taixe, Xavier Alameda-Pineda

In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers.

Multi-Object Tracking Multiple Object Tracking

Understanding the Limitations of CNN-based Absolute Camera Pose Regression

1 code implementation CVPR 2019 Torsten Sattler, Qunjie Zhou, Marc Pollefeys, Laura Leal-Taixe

We furthermore use our model to show that pose regression is more closely related to pose approximation via image retrieval than to accurate pose estimation via 3D structure.

Image Retrieval Mixed Reality +5

Tracking without bells and whistles

9 code implementations ICCV 2019 Philipp Bergmann, Tim Meinhardt, Laura Leal-Taixe

Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions.

Motion Compensation motion prediction +1

Lifting Layers: Analysis and Applications

1 code implementation ECCV 2018 Peter Ochs, Tim Meinhardt, Laura Leal-Taixe, Michael Moeller

A lifting layer increases the dimensionality of the input, naturally yields a linear spline when combined with a fully connected layer, and therefore closes the gap between low and high dimensional approximation problems.

Denoising Image Classification

Automatic tracking of vessel-like structures from a single starting point

no code implementations8 Jun 2017 Dario Augusto Borges Oliveira, Laura Leal-Taixe, Raul Queiroz Feitosa, Bodo Rosenhahn

Further visual results also show the potential of our approach for identifying vascular networks topologies.

MOT16: A Benchmark for Multi-Object Tracking

7 code implementations2 Mar 2016 Anton Milan, Laura Leal-Taixe, Ian Reid, Stefan Roth, Konrad Schindler

Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods.

Multi-Object Tracking Multiple Object Tracking +1

Continuous Pose Estimation With a Spatial Ensemble of Fisher Regressors

no code implementations ICCV 2015 Michele Fenzi, Laura Leal-Taixe, Jorn Ostermann, Tinne Tuytelaars

In this paper, we treat the problem of continuous pose estimation for object categories as a regression problem on the basis of only 2D training information.

Pose Estimation regression

Joint Tracking and Segmentation of Multiple Targets

no code implementations CVPR 2015 Anton Milan, Laura Leal-Taixe, Konrad Schindler, Ian Reid

Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios.

Video Segmentation Video Semantic Segmentation

Learning an Image-based Motion Context for Multiple People Tracking

no code implementations CVPR 2014 Laura Leal-Taixe, Michele Fenzi, Alina Kuznetsova, Bodo Rosenhahn, Silvio Savarese

We present a novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals.

Multiple People Tracking

Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories

no code implementations CVPR 2013 Michele Fenzi, Laura Leal-Taixe, Bodo Rosenhahn, Jorn Ostermann

In this paper, we propose a method for learning a class representation that can return a continuous value for the pose of an unknown class instance using only 2D data and weak 3D labelling information.

Pose Estimation regression

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