Search Results for author: Andrew Rabinovich

Found 24 papers, 14 papers with code

MagicEyes: A Large Scale Eye Gaze Estimation Dataset for Mixed Reality

no code implementations18 Mar 2020 Zhengyang Wu, Srivignesh Rajendran, Tarrence van As, Joelle Zimmermann, Vijay Badrinarayanan, Andrew Rabinovich

With the emergence of Virtual and Mixed Reality (XR) devices, eye tracking has received significant attention in the computer vision community.

Eye Tracking Gaze Estimation +1

Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes

no code implementations16 Mar 2020 Ameya Phalak, Vijay Badrinarayanan, Andrew Rabinovich

We introduce Scan2Plan, a novel approach for accurate estimation of a floorplan from a 3D scan of the structural elements of indoor environments.

SuperGlue: Learning Feature Matching with Graph Neural Networks

13 code implementations CVPR 2020 Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.

Pose Estimation Visual Place Recognition

EyeNet: A Multi-Task Network for Off-Axis Eye Gaze Estimation and User Understanding

no code implementations24 Aug 2019 Zhengyang Wu, Srivignesh Rajendran, Tarrence van As, Joelle Zimmermann, Vijay Badrinarayanan, Andrew Rabinovich

Eye gaze estimation and simultaneous semantic understanding of a user through eye images is a crucial component in Virtual and Mixed Reality; enabling energy efficient rendering, multi-focal displays and effective interaction with 3D content.

Gaze Estimation Mixed Reality

DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences

no code implementations25 Apr 2019 Ameya Phalak, Zhao Chen, Darvin Yi, Khushi Gupta, Vijay Badrinarayanan, Andrew Rabinovich

We present DeepPerimeter, a deep learning based pipeline for inferring a full indoor perimeter (i. e. exterior boundary map) from a sequence of posed RGB images.

Depth Estimation

Self-Improving Visual Odometry

no code implementations8 Dec 2018 Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a network which computes pointwise data associations across images.

Pose Estimation Self-Supervised Learning +1

Gradient Adversarial Training of Neural Networks

no code implementations21 Jun 2018 Ayan Sinha, Zhao Chen, Vijay Badrinarayanan, Andrew Rabinovich

We demonstrate gradient adversarial training for three different scenarios: (1) as a defense to adversarial examples we classify gradient tensors and tune them to be agnostic to the class of their corresponding example, (2) for knowledge distillation, we do binary classification of gradient tensors derived from the student or teacher network and tune the student gradient tensor to mimic the teacher's gradient tensor; and (3) for multi-task learning we classify the gradient tensors derived from different task loss functions and tune them to be statistically indistinguishable.

Knowledge Distillation Multi-Task Learning

Estimating Depth from RGB and Sparse Sensing

2 code implementations ECCV 2018 Zhao Chen, Vijay Badrinarayanan, Gilad Drozdov, Andrew Rabinovich

We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels.

Monocular Depth Estimation

SuperPoint: Self-Supervised Interest Point Detection and Description

17 code implementations20 Dec 2017 Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision.

Domain Adaptation Homography Estimation

GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks

4 code implementations ICML 2018 Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich

Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly.

Toward Geometric Deep SLAM

no code implementations24 Jul 2017 Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

The first network, MagicPoint, operates on single images and extracts salient 2D points.

Deep Cuboid Detection: Beyond 2D Bounding Boxes

1 code implementation30 Nov 2016 Debidatta Dwibedi, Tomasz Malisiewicz, Vijay Badrinarayanan, Andrew Rabinovich

We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects).

Deep Image Homography Estimation

8 code implementations13 Jun 2016 Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

We present a deep convolutional neural network for estimating the relative homography between a pair of images.

Homography Estimation

ParseNet: Looking Wider to See Better

4 code implementations15 Jun 2015 Wei Liu, Andrew Rabinovich, Alexander C. Berg

When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines.

Semantic Segmentation

What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

1 code implementation5 Mar 2015 Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, Kevin Murphy

We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task.

Keyword Spotting

Self-informed neural network structure learning

no code implementations20 Dec 2014 David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov

We study the problem of large scale, multi-label visual recognition with a large number of possible classes.

Object Recognition

Going Deeper with Convolutions

65 code implementations CVPR 2015 Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).

Classification General Classification +3

Using Web Co-occurrence Statistics for Improving Image Categorization

no code implementations19 Dec 2013 Samy Bengio, Jeff Dean, Dumitru Erhan, Eugene Ie, Quoc Le, Andrew Rabinovich, Jonathon Shlens, Yoram Singer

Albeit the simplicity of the resulting optimization problem, it is effective in improving both recognition and localization accuracy.

Common Sense Reasoning Image Categorization +1

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