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Just Label What You Need: Fine-Grained Active Selection for Perception
  and Prediction through Partially Labeled Scenes

Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes

8 April 2021
Sean Segal
Nishanth Kumar
Sergio Casas
Wenyuan Zeng
Mengye Ren
Jingkang Wang
R. Urtasun
ArXivPDFHTML

Papers citing "Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes"

5 / 5 papers shown
Title
IntentNet: Learning to Predict Intention from Raw Sensor Data
IntentNet: Learning to Predict Intention from Raw Sensor Data
Sergio Casas
Wenjie Luo
R. Urtasun
3DPC
152
365
0
20 Jan 2021
Diverse Complexity Measures for Dataset Curation in Self-driving
Diverse Complexity Measures for Dataset Curation in Self-driving
Abbas Sadat
Sean Segal
Sergio Casas
James Tu
Binh Yang
R. Urtasun
Ersin Yumer
42
13
0
16 Jan 2021
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
Sergio Casas
Cole Gulino
Simon Suo
Katie Z Luo
Renjie Liao
R. Urtasun
147
155
0
23 Jul 2020
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Ajay Jain
Sergio Casas
Renjie Liao
Yuwen Xiong
Song Feng
Sean Segal
R. Urtasun
138
73
0
17 Oct 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,660
0
05 Dec 2016
1