Bharath Hariharan

research

∙ 09/23/2022

Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs

A self-driving car must be able to reliably handle adverse weather condi...

Youya Xia, et al. ∙

research

∙ 08/01/2022

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

Advances in perception for self-driving cars have accelerated in recent ...

Carlos A. Diaz-Ruiz, et al. ∙

research

∙ 07/07/2022

Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners

Few-shot recognition involves training an image classifier to distinguis...

Davis Wertheimer, et al. ∙

research

∙ 04/14/2022

Activation Regression for Continuous Domain Generalization with Applications to Crop Classification

Geographic variance in satellite imagery impacts the ability of machine ...

Samar Khanna, et al. ∙

research

∙ 03/29/2022

Learning to Detect Mobile Objects from LiDAR Scans Without Labels

Current 3D object detectors for autonomous driving are almost entirely t...

Yurong You, et al. ∙

research

∙ 03/23/2022

Visual Prompt Tuning

The current modus operandi in adapting pre-trained models involves updat...

Menglin Jia, et al. ∙

research

∙ 03/22/2022

Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception

Self-driving cars must detect vehicles, pedestrians, and other traffic p...

Yurong You, et al. ∙

research

∙ 03/16/2022

Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Unsupervised semantic segmentation aims to discover and localize semanti...

Mark Hamilton, et al. ∙

research

∙ 02/26/2022

Orientation-Discriminative Feature Representation for Decentralized Pedestrian Tracking

This paper focuses on the problem of decentralized pedestrian tracking u...

Vikram Shree, et al. ∙

research

∙ 02/01/2022

Stay Positive: Non-Negative Image Synthesis for Augmented Reality

In applications such as optical see-through and projector augmented real...

Katie Luo, et al. ∙

research

∙ 08/24/2021

Field-Guide-Inspired Zero-Shot Learning

Modern recognition systems require large amounts of supervision to achie...

Utkarsh Mall, et al. ∙

research

∙ 04/28/2021

Extreme Rotation Estimation using Dense Correlation Volumes

We present a technique for estimating the relative 3D rotation of an RGB...

Ruojin Cai, et al. ∙

research

∙ 03/30/2021

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

We present a new framework for semantic segmentation without annotations...

Jang Hyun Cho, et al. ∙

research

∙ 03/26/2021

Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection

Self-driving cars must detect other vehicles and pedestrians in 3D to pl...

Yurong You, et al. ∙

research

∙ 12/02/2020

Fine-Grained Few-Shot Classification with Feature Map Reconstruction Networks

In this paper we reformulate few-shot classification as a reconstruction...

Davis Wertheimer, et al. ∙

research

∙ 11/25/2020

Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation

We aim to build image generation models that generalize to new domains f...

Davis Wertheimer, et al. ∙

research

∙ 10/15/2020

Self-training for Few-shot Transfer Across Extreme Task Differences

All few-shot learning techniques must be pre-trained on a large, labeled...

Cheng Perng Phoo, et al. ∙

research

∙ 08/14/2020

Learning Gradient Fields for Shape Generation

In this work, we propose a novel technique to generate shapes from point...

Ruojin Cai, et al. ∙

research

∙ 07/06/2020

Wasserstein Distances for Stereo Disparity Estimation

Existing approaches to depth or disparity estimation output a distributi...

Divyansh Garg, et al. ∙

research

∙ 05/17/2020

Train in Germany, Test in The USA: Making 3D Object Detectors Generalize

In the domain of autonomous driving, deep learning has substantially imp...

Yan Wang, et al. ∙

research

∙ 04/28/2020

Learning Feature Descriptors using Camera Pose Supervision

Recent research on learned visual descriptors has shown promising improv...

Qianqian Wang, et al. ∙

research

∙ 04/26/2020

Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

In this work we explore the task of instance segmentation with attribute...

Menglin Jia, et al. ∙

research

∙ 04/24/2020

Extending and Analyzing Self-Supervised Learning Across Domains

Self-supervised representation learning has achieved impressive results ...

Bram Wallace, et al. ∙

research

∙ 04/07/2020

End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

Reliable and accurate 3D object detection is a necessity for safe autono...

Rui Qian, et al. ∙

research

∙ 04/01/2020

Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition

Few-shot, fine-grained classification requires a model to learn subtle, ...

Luming Tang, et al. ∙

research

∙ 10/30/2019

LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images

Object segmentation in three-dimensional (3-D) point clouds is a critica...

Brian H. Wang, et al. ∙

research

∙ 10/08/2019

When Does Self-supervision Improve Few-shot Learning?

We present a technique to improve the generalization of deep representat...

Jong-Chyi Su, et al. ∙

research

∙ 10/03/2019

On the Efficacy of Knowledge Distillation

In this paper, we present a thorough evaluation of the efficacy of knowl...

Jang Hyun Cho, et al. ∙

research

∙ 09/03/2019

Few-Shot Generalization for Single-Image 3D Reconstruction via Priors

Recent work on single-view 3D reconstruction shows impressive results, b...

Bram Wallace, et al. ∙

research

∙ 08/29/2019

GeoStyle: Discovering Fashion Trends and Events

Understanding fashion styles and trends is of great potential interest t...

Utkarsh Mall, et al. ∙

research

∙ 06/28/2019

PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows

As 3D point clouds become the representation of choice for multiple visi...

Guandao Yang, et al. ∙

research

∙ 06/17/2019

Boosting Supervision with Self-Supervision for Few-shot Learning

We present a technique to improve the transferability of deep representa...

Jong-Chyi Su, et al. ∙

research

∙ 06/14/2019

Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

Detecting objects such as cars and pedestrians in 3D plays an indispensa...

Yurong You, et al. ∙

research

∙ 04/09/2019

Few-Shot Learning with Localization in Realistic Settings

Traditional recognition methods typically require large, artificially-ba...

Davis Wertheimer, et al. ∙

research

∙ 12/18/2018

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

3D object detection is an essential task in autonomous driving. Recent t...

Yan Wang, et al. ∙

research

∙ 10/24/2018

A Deep-Learning-Based Fashion Attributes Detection Model

Analyzing fashion attributes is essential in the fashion design process....

Menglin Jia, et al. ∙

research

∙ 10/03/2018

Deep Fundamental Matrix Estimation without Correspondences

Estimating fundamental matrices is a classic problem in computer vision....

Omid Poursaeed, et al. ∙

research

∙ 05/22/2018

Resource Aware Person Re-identification across Multiple Resolutions

Not all people are equally easy to identify: color statistics might be e...

Yan Wang, et al. ∙

research

∙ 01/16/2018

Low-Shot Learning from Imaginary Data

Humans can quickly learn new visual concepts, perhaps because they can e...

Yu-Xiong Wang, et al. ∙

research

∙ 06/07/2017

Low-shot learning with large-scale diffusion

This paper considers the problem of inferring image labels for which onl...

Matthijs Douze, et al. ∙

research

∙ 05/10/2017

Inferring and Executing Programs for Visual Reasoning

Existing methods for visual reasoning attempt to directly map inputs to ...

Justin Johnson, et al. ∙

research

∙ 12/20/2016

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

When building artificial intelligence systems that can reason and answer...

Justin Johnson, et al. ∙

research

∙ 12/19/2016

Learning Features by Watching Objects Move

This paper presents a novel yet intuitive approach to unsupervised featu...

Deepak Pathak, et al. ∙

research

∙ 12/09/2016

Feature Pyramid Networks for Object Detection

Feature pyramids are a basic component in recognition systems for detect...

Tsung-Yi Lin, et al. ∙

research

∙ 06/09/2016

Low-shot Visual Recognition by Shrinking and Hallucinating Features

Low-shot visual learning---the ability to recognize novel object categor...

Bharath Hariharan, et al. ∙

research

∙ 11/26/2015

Iterative Instance Segmentation

Existing methods for pixel-wise labelling tasks generally disregard the ...

Ke Li, et al. ∙

research

∙ 05/08/2015

DeepBox: Learning Objectness with Convolutional Networks

Existing object proposal approaches use primarily bottom-up cues to rank...

Weicheng Kuo, et al. ∙

research

∙ 11/21/2014

Hypercolumns for Object Segmentation and Fine-grained Localization

Recognition algorithms based on convolutional networks (CNNs) typically ...

Bharath Hariharan, et al. ∙

research

∙ 07/07/2014

Simultaneous Detection and Segmentation

We aim to detect all instances of a category in an image and, for each i...

Bharath Hariharan, et al. ∙

research

∙ 06/19/2014

R-CNNs for Pose Estimation and Action Detection

We present convolutional neural networks for the tasks of keypoint (pose...

Georgia Gkioxari, et al. ∙

Bharath Hariharan

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