Search Results for author: Bharath Hariharan

Found 48 papers, 33 papers with code

Geometry Processing with Neural Fields

1 code implementation NeurIPS 2021 Guandao Yang, Serge Belongie, Bharath Hariharan, Vladlen Koltun

Most existing geometry processing algorithms use meshes as the default shape representation.

Field-Guide-Inspired Zero-Shot Learning

no code implementations ICCV 2021 Utkarsh Mall, Bharath Hariharan, Kavita Bala

Annotating the full set of attributes for a novel category proves to be a tedious and expensive task in deployment.

Zero-Shot Learning

Stay Positive: Non-Negative Image Synthesis for Augmented Reality

no code implementations CVPR 2021 Katie Luo, Guandao Yang, Wenqi Xian, Harald Haraldsson, Bharath Hariharan, Serge Belongie

In applications such as optical see-through and projector augmented reality, producing images amounts to solving non-negative image generation, where one can only add light to an existing image.

Image-to-Image Translation Style Transfer +1

Can We Characterize Tasks Without Labels or Features?

1 code implementation CVPR 2021 Bram Wallace, Ziyang Wu, Bharath Hariharan

The problem of expert model selection deals with choosing the appropriate pretrained network ("expert") to transfer to a target task.

Model Selection

Extreme Rotation Estimation using Dense Correlation Volumes

1 code implementation CVPR 2021 Ruojin Cai, Bharath Hariharan, Noah Snavely, Hadar Averbuch-Elor

We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap.

Coarsely-Labeled Data for Better Few-Shot Transfer

no code implementations ICCV 2021 Cheng Perng Phoo, Bharath Hariharan

Few-shot learning is based on the premise that labels are expensive, especially when they are fine-grained and require expertise.

Few-Shot Learning Representation Learning

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

1 code implementation CVPR 2020 Yan Wang, Xiangyu Chen, Yurong You, Li Erran, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao

In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike.

3D Object Detection Autonomous Driving

Learning Feature Descriptors using Camera Pose Supervision

1 code implementation ECCV 2020 Qianqian Wang, Xiaowei Zhou, Bharath Hariharan, Noah Snavely

Recent research on learned visual descriptors has shown promising improvements in correspondence estimation, a key component of many 3D vision tasks.

Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

3 code implementations ECCV 2020 Menglin Jia, Mengyun Shi, Mikhail Sirotenko, Yin Cui, Claire Cardie, Bharath Hariharan, Hartwig Adam, Serge Belongie

In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes).

Fine-Grained Visual Categorization Fine-Grained Visual Recognition +3

Extending and Analyzing Self-Supervised Learning Across Domains

1 code implementation ECCV 2020 Bram Wallace, Bharath Hariharan

There has been little to no work with these methods on other smaller domains, such as satellite, textural, or biological imagery.

Representation Learning Self-Supervised Learning

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

1 code implementation CVPR 2020 Luming Tang, Davis Wertheimer, Bharath Hariharan

Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e. g., birds) based on a few images alone.

General Classification

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

1 code implementation30 Oct 2019 Brian H. Wang, Wei-Lun Chao, Yan Wang, Bharath Hariharan, Kilian Q. Weinberger, Mark Campbell

We obtain 2-D segmentation predictions by applying Mask-RCNN to the RGB image, and then link this image to a 3-D lidar point cloud by building a graph of connections among 3-D points and 2-D pixels.

Point Cloud Segmentation Semantic Segmentation

On the Efficacy of Knowledge Distillation

no code implementations ICCV 2019 Jang Hyun Cho, Bharath Hariharan

In this paper, we present a thorough evaluation of the efficacy of knowledge distillation and its dependence on student and teacher architectures.

Knowledge Distillation

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

no code implementations ICCV 2019 Bram Wallace, Bharath Hariharan

To address this problem, we present a new model architecture that reframes single-view 3D reconstruction as learnt, category agnostic refinement of a provided, category-specific prior.

3D Reconstruction Single-View 3D Reconstruction

GeoStyle: Discovering Fashion Trends and Events

1 code implementation ICCV 2019 Utkarsh Mall, Kevin Matzen, Bharath Hariharan, Noah Snavely, Kavita Bala

Understanding fashion styles and trends is of great potential interest to retailers and consumers alike.

PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows

11 code implementations ICCV 2019 Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, Bharath Hariharan

Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.

Point Cloud Generation Variational Inference

Boosting Supervision with Self-Supervision for Few-shot Learning

no code implementations17 Jun 2019 Jong-Chyi Su, Subhransu Maji, Bharath Hariharan

We present a technique to improve the transferability of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions.

Few-Shot Learning Self-Supervised Learning

Few-Shot Learning with Localization in Realistic Settings

1 code implementation CVPR 2019 Davis Wertheimer, Bharath Hariharan

Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones.

Few-Shot Learning

A Deep-Learning-Based Fashion Attributes Detection Model

1 code implementation24 Oct 2018 Menglin Jia, Yichen Zhou, Mengyun Shi, Bharath Hariharan

Such information analyzing process is called abstracting, which recognize similarities or differences across all the garments and collections.

Resource Aware Person Re-identification across Multiple Resolutions

1 code implementation CVPR 2018 Yan Wang, Lequn Wang, Yurong You, Xu Zou, Vincent Chen, Serena Li, Gao Huang, Bharath Hariharan, Kilian Q. Weinberger

Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about high- and low-level details.

Person Re-Identification

Low-Shot Learning from Imaginary Data

1 code implementation CVPR 2018 Yu-Xiong Wang, Ross Girshick, Martial Hebert, Bharath Hariharan

Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views.

General Classification Meta-Learning

Low-shot learning with large-scale diffusion

1 code implementation CVPR 2018 Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou

This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time.

graph construction

Inferring and Executing Programs for Visual Reasoning

5 code implementations ICCV 2017 Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick

Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.

Visual Question Answering Visual Reasoning

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

4 code implementations CVPR 2017 Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick

When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings.

Question Answering Visual Question Answering +1

Learning Features by Watching Objects Move

1 code implementation CVPR 2017 Deepak Pathak, Ross Girshick, Piotr Dollár, Trevor Darrell, Bharath Hariharan

Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed 'pretext' tasks studied in the literature.

Object Detection Transfer Learning

Low-shot Visual Recognition by Shrinking and Hallucinating Features

4 code implementations ICCV 2017 Bharath Hariharan, Ross Girshick

Low-shot visual learning---the ability to recognize novel object categories from very few examples---is a hallmark of human visual intelligence.

Iterative Instance Segmentation

no code implementations CVPR 2016 Ke Li, Bharath Hariharan, Jitendra Malik

Existing methods for pixel-wise labelling tasks generally disregard the underlying structure of labellings, often leading to predictions that are visually implausible.

Instance Segmentation Semantic Segmentation +1

DeepBox: Learning Objectness with Convolutional Networks

1 code implementation ICCV 2015 Wei-cheng Kuo, Bharath Hariharan, Jitendra Malik

Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that objectness is in fact a high level construct.

Hypercolumns for Object Segmentation and Fine-grained Localization

6 code implementations CVPR 2015 Bharath Hariharan, Pablo Arbeláez, Ross Girshick, Jitendra Malik

Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as feature representation.

Semantic Segmentation

R-CNNs for Pose Estimation and Action Detection

no code implementations19 Jun 2014 Georgia Gkioxari, Bharath Hariharan, Ross Girshick, Jitendra Malik

We present convolutional neural networks for the tasks of keypoint (pose) prediction and action classification of people in unconstrained images.

Action Classification Action Detection +3

Using k-Poselets for Detecting People and Localizing Their Keypoints

no code implementations CVPR 2014 Georgia Gkioxari, Bharath Hariharan, Ross Girshick, Jitendra Malik

A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations.

Human Detection

Detecting Objects using Deformation Dictionaries

no code implementations CVPR 2014 Bharath Hariharan, C. L. Zitnick, Piotr Dollar

Several popular and effective object detectors separately model intra-class variations arising from deformations and appearance changes.

Cannot find the paper you are looking for? You can Submit a new open access paper.