1.4k citations · 4.9k across the 44 of their papers we have counts for
12 papers · 1 filter
MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?
Matteo Fabbri, Guillem Braso, Gianluca Maugeri +6
Deep learning-based methods for video pedestrian detection and tracking require large volumes of training data to achieve good performance. However, data acquisition in crowded pub…
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction
Patrick Dendorfer, Sven Elflein, Laura Leal-Taixé
Pedestrian trajectory prediction is challenging due to its uncertain and multimodal nature. While generative adversarial networks can learn a distribution over future trajectories,…
(Just) A Spoonful of Refinements Helps the Registration Error Go Down
Sérgio Agostinho, Aljoša Ošep, Alessio Del Bue +1
We tackle data-driven 3D point cloud registration. Given point correspondences, the standard Kabsch algorithm provides an optimal rotation estimate. This allows to train registrati…
DeepLab2: A TensorFlow Library for Deep Labeling
Mark Weber, Huiyu Wang, Siyuan Qiao +12
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…
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection
Ismail Elezi, Zhiding Yu, Anima Anandkumar +2
Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the labels dependency, various active learn…
The 2021 Image Similarity Dataset and Challenge
Matthijs Douze, Giorgos Tolias, Ed Pizzi +9
This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to…
EagerMOT: 3D Multi-Object Tracking via Sensor Fusion
Aleksandr Kim, Aljoša Ošep, Laura Leal-Taixé
Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time. Existing methods r…
Opening up Open-World Tracking
Yang Liu, Idil Esen Zulfikar, Jonathon Luiten +5
Tracking and detecting any object, including ones never-seen-before during model training, is a crucial but elusive capability of autonomous systems. An autonomous agent that is bl…
Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning
Patrick Wenzel, Torsten Schön, Laura Leal-Taixé +1
Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this paper, we consider the problem of obstacle avoidance in simple 3D en…
4D Panoptic LiDAR Segmentation
Mehmet Aygün, Aljoša Ošep, Mark Weber +4
Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to ass…
STEP: Segmenting and Tracking Every Pixel
Mark Weber, Jun Xie, Maxwell Collins +10
The task of assigning semantic classes and track identities to every pixel in a video is called video panoptic segmentation. Our work is the first that targets this task in a real-…
TrackFormer: Multi-Object Tracking with Transformers
Tim Meinhardt, Alexander Kirillov, Laura Leal-Taixe +1
The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories. We formulate this task a…