Search Results for author: Alireza Fathi

Found 17 papers, 7 papers with code

Object-Centric Neural Scene Rendering

no code implementations15 Dec 2020 Michelle Guo, Alireza Fathi, Jiajun Wu, Thomas Funkhouser

We present a method for composing photorealistic scenes from captured images of objects.

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

no code implementations24 Sep 2020 Yue Wang, Alireza Fathi, Jiajun Wu, Thomas Funkhouser, Justin Solomon

A common dilemma in 3D object detection for autonomous driving is that high-quality, dense point clouds are only available during training, but not testing.

3D Object Detection Autonomous Driving +1

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

1 code implementation30 Mar 2020 Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner

We show that grouping proposals improves over NMS and outperforms previous state-of-the-art methods on the tasks of 3D object detection and semantic instance segmentation on the ScanNetV2 benchmark and the S3DIS dataset.

3D Instance Segmentation 3D Object Detection +1

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

1 code implementation16 Jun 2019 Steven Hickson, Karthik Raveendran, Alireza Fathi, Kevin Murphy, Irfan Essa

We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image.

Semantic Segmentation Surface Normals Estimation

Instance Embedding Transfer to Unsupervised Video Object Segmentation

no code implementations CVPR 2018 Siyang Li, Bryan Seybold, Alexey Vorobyov, Alireza Fathi, Qin Huang, C. -C. Jay Kuo

We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks.

Fine-tuning Optical Flow Estimation +3

The Devil is in the Decoder: Classification, Regression and GANs

1 code implementation18 Jul 2017 Zbigniew Wojna, Vittorio Ferrari, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, Jasper Uijlings

Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image.

Boundary Detection Classification +3

Semantic Instance Segmentation via Deep Metric Learning

1 code implementation30 Mar 2017 Alireza Fathi, Zbigniew Wojna, Vivek Rathod, Peng Wang, Hyun Oh Song, Sergio Guadarrama, Kevin P. Murphy

We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.

Instance Segmentation Metric Learning +2

Speed/accuracy trade-offs for modern convolutional object detectors

13 code implementations CVPR 2017 Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang song, Sergio Guadarrama, Kevin Murphy

On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.

Ranked #174 on Object Detection on COCO test-dev (using extra training data)

Object Detection

VideoSET: Video Summary Evaluation through Text

no code implementations23 Jun 2014 Serena Yeung, Alireza Fathi, Li Fei-Fei

In this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information contained in its original video.

An introduction to synchronous self-learning Pareto strategy

no code implementations15 Dec 2013 Ahmad Mozaffari, Alireza Fathi

In last decades optimization and control of complex systems that possessed various conflicted objectives simultaneously attracted an incremental interest of scientists.

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

no code implementations14 Dec 2013 Ahmad Mozaffari, Alireza Fathi

The obtained results confirm the acceptable performance of the proposed method in both robustness and quality for different bench-mark optimizing problems and also prove the authors claim.

Fault Detection

Modeling Actions through State Changes

no code implementations CVPR 2013 Alireza Fathi, James M. Rehg

The key to differentiating these actions is the ability to identify how they change the state of objects and materials in the environment.

Action Recognition

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