Alireza Fathi

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Google ∙ Georgia Institute of Technology

Object-Centric Neural Scene Rendering

We present a method for composing photorealistic scenes from captured im...
12/15/2020 ∙ by Michelle Guo, et al. ∙ 8 ∙ share

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Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection

A common dilemma in 3D object detection for autonomous driving is that h...
09/24/2020 ∙ by Yue Wang, et al. ∙ 0 ∙ share

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Virtual Multi-view Fusion for 3D Semantic Segmentation

Semantic segmentation of 3D meshes is an important problem for 3D scene ...
07/26/2020 ∙ by Abhijit Kundu, et al. ∙ 2 ∙ share

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An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds

Detecting objects in 3D LiDAR data is a core technology for autonomous d...
07/24/2020 ∙ by Rui Huang, et al. ∙ 35 ∙ share

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Pillar-based Object Detection for Autonomous Driving

We present a simple and flexible object detection framework optimized fo...
07/20/2020 ∙ by Yue Wang, et al. ∙ 10 ∙ share

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DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes

We propose DOPS, a fast single-stage 3D object detection method for LIDA...
04/02/2020 ∙ by Mahyar Najibi, et al. ∙ 0 ∙ share

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3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation

We present 3D-MPA, a method for instance segmentation on 3D point clouds...
03/30/2020 ∙ by Francis Engelmann, et al. ∙ 3 ∙ share

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Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction

We propose 4 insights that help to significantly improve the performance...
06/16/2019 ∙ by Steven Hickson, et al. ∙ 4 ∙ share

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Tracking Emerges by Colorizing Videos

We use large amounts of unlabeled video to learn models for visual track...
06/25/2018 ∙ by Carl Vondrick, et al. ∙ 0 ∙ share

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Instance Embedding Transfer to Unsupervised Video Object Segmentation

We propose a method for unsupervised video object segmentation by transf...
01/03/2018 ∙ by Siyang Li, et al. ∙ 0 ∙ share

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The Devil is in the Decoder

Many machine vision applications require predictions for every pixel of ...
07/18/2017 ∙ by Zbigniew Wojna, et al. ∙ 0 ∙ share

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Semantic Instance Segmentation via Deep Metric Learning

We propose a new method for semantic instance segmentation, by first com...
03/30/2017 ∙ by Alireza Fathi, et al. ∙ 0 ∙ share

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Speed/accuracy trade-offs for modern convolutional object detectors

The goal of this paper is to serve as a guide for selecting a detection ...
11/30/2016 ∙ by Jonathan Huang, et al. ∙ 0 ∙ share

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VideoSET: Video Summary Evaluation through Text

In this paper we present VideoSET, a method for Video Summary Evaluation...
06/23/2014 ∙ by Serena Yeung, et al. ∙ 0 ∙ share

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An introduction to synchronous self-learning Pareto strategy

In last decades optimization and control of complex systems that possess...
12/15/2013 ∙ by Ahmad Mozaffari, et al. ∙ 0 ∙ share

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A natural-inspired optimization machine based on the annual migration of salmons in nature

Bio inspiration is a branch of artificial simulation science that shows ...
12/14/2013 ∙ by Ahmad Mozaffari, et al. ∙ 0 ∙ share

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Alireza Fathi

Featured Co-authors

Yue Wang

Peng Wang

Li Fei-Fei

C. -C. Jay Kuo

Larry S. Davis

Matthias Nießner

Jiajun Wu

Kevin Murphy

Vittorio Ferrari

Bastian Leibe

Yang Song

Object-Centric Neural Scene Rendering

Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection

Virtual Multi-view Fusion for 3D Semantic Segmentation

An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds

Pillar-based Object Detection for Autonomous Driving

DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes

3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation

Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction

Tracking Emerges by Colorizing Videos

Instance Embedding Transfer to Unsupervised Video Object Segmentation

The Devil is in the Decoder

Semantic Instance Segmentation via Deep Metric Learning

Speed/accuracy trade-offs for modern convolutional object detectors

VideoSET: Video Summary Evaluation through Text

An introduction to synchronous self-learning Pareto strategy

A natural-inspired optimization machine based on the annual migration of salmons in nature