Emergent Correspondence from Image Diffusion
Luming Tang*, Menglin Jia*, Qianqian Wang*, Cheng Phoo, Bharath Hariharan
In Submitted, 2023
TLDR : Semantic and geometric correspondences can be extracted from diffusion models without any further training
pdf bibtex
```
@inproceedings{Tang2023Emergent,
title = {Emergent Correspondence from Image Diffusion},
author = {Luming Tang and  Menglin Jia and  Qianqian Wang and  Cheng Phoo and  Bharath Hariharan},
year = {2023},
booktitle = {Submitted}
}
```
Distilling from Similar Tasks for Transfer Learning on a Budget
Kenneth Borup, Cheng Perng Phoo, Bharath Hariharan
In ICCV, 2023
TLDR : We train models for new domains with limited data and limited compute by identifying pre-training domains and distilling from them
pdf bibtex
```
@inproceedings{Borup2023Distilling,
title = {Distilling from Similar Tasks for Transfer Learning on a Budget},
author = {Kenneth Borup and  Cheng Perng Phoo and  Bharath Hariharan},
year = {2023},
booktitle = {ICCV}
}
```
Change-Aware Sampling and Contrastive Learning for Satellite Images
Utkarsh Mall, Bharath Hariharan, Kavita Bala
In CVPR, 2023
TLDR : A self-supervised learning approach for satellite images that pushes apart images taken from the same location but over long time spans
pdf bibtex
```
@inproceedings{Mall2023Change,
title = {Change-Aware Sampling and Contrastive Learning for Satellite Images},
author = {Utkarsh Mall and  Bharath Hariharan and  Kavita Bala},
year = {2023},
booktitle = {CVPR}
}
```
Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery
Utkarsh Mall, Bharath Hariharan, Kavita Bala
In NeurIPS (Datasets and Benchmarks track), 2022
TLDR : A new benchmark on discovering meaningful multi-step change events from satellite images with few/no labels
pdf bibtex
```
@inproceedings{Mall2022Change,
title = {Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery},
author = {Utkarsh Mall and  Bharath Hariharan and  Kavita Bala},
year = {2022},
booktitle = {NeurIPS (Datasets and Benchmarks track)}
}
```
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
Yurong You*, Cheng Perng Phoo*, Katie Z Luo*, Travis Zhang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
In NeurIPS, 2022
TLDR : Data captured from repeated traversals of a scene can be used to adapt 3D object detectors to new domains without supervision
pdf bibtex
```
@inproceedings{You2022Unsupervised,
title = {Unsupervised Adaptation from Repeated Traversals for Autonomous Driving},
author = {Yurong You and  Cheng Perng Phoo and  Katie Z Luo and  Travis Zhang and  Wei-Lun Chao and  Bharath Hariharan and  Mark Campbell and  Kilian Q. Weinberger},
year = {2022},
booktitle = {NeurIPS}
}
```

Visual Prompt Tuning
Menglin Jia*, Luming Tang*, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, Ser-Nam Lim
In ECCV, 2022
TLDR : Learning a prompt is the best way to transfer vision transformers to new tasks
pdf bibtex

@inproceedings{Jia2022Visual,
title = {Visual Prompt Tuning},
author = {Menglin Jia and  Luming Tang and  Bor-Chun Chen and  Claire Cardie and  Serge Belongie and  Bharath Hariharan and  Ser-Nam Lim},
year = {2022},
booktitle = {ECCV}
}

Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection
Yurong You*, Carlos Andres Diaz-Ruiz*, Yan Wang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Weinberger
In ICRA, 2022
TLDR : Extrapolating tracks of detected objects yields good ground truth for adapting 3D detectors
pdf bibtex

@inproceedings{You2022Exploiting,
title = {Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection},
author = {Yurong You and   Carlos Andres Diaz-Ruiz and  Yan Wang and  Wei-Lun Chao and  Bharath Hariharan and  Mark Campbell and  Kilian Weinberger},
year = {2022},
booktitle = {ICRA}
}

Learning to Detect Mobile Objects from LiDAR Scans Without Labels
Yurong You*, Katie Luo*, Cheng Perng Phoo, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, Kilian Weinberger
In CVPR, 2022
TLDR : Can we train a 3D object detector without labels by simply driving around?
pdf bibtex

@inproceedings{You2022Learning,
title = {Learning to Detect Mobile Objects from LiDAR Scans Without Labels},
author = {Yurong You and  Katie Luo and  Cheng Perng Phoo and  Wei-Lun Chao and  Wen Sun and  Bharath Hariharan and  Mark Campbell and  Kilian Weinberger},
year = {2022},
booktitle = {CVPR}
}

Coarsely-labeled Data for Better Few-shot Transfer
Cheng Perng Phoo, Bharath Hariharan
In ICCV, 2021
TLDR : Coarse labels are cheap to acquire and can boost few-shot learning
pdf bibtex

@inproceedings{Phoo2021Coarsely,
title = {Coarsely-labeled Data for Better Few-shot Transfer},
author = {Cheng Perng Phoo and  Bharath Hariharan},
year = {2021},
booktitle = {ICCV}
}

Field Guide-inspired Zero-Shot Learning
Utkarsh Mall, Bharath Hariharan, Kavita Bala
In ICCV, 2021
TLDR : A more usable, active-learning based interface for specifying hundreds of attributes in zero-shot learning
pdf bibtex
```
@inproceedings{Mall2021Field,
title = {Field Guide-inspired Zero-Shot Learning},
author = {Utkarsh Mall and  Bharath Hariharan and  Kavita Bala},
year = {2021},
booktitle = {ICCV}
}
```
Can We Characterize Tasks Without Labels or Features?
Bram Wallace, Ziyang Wu, Bharath Hariharan
In CVPR, 2021
TLDR : You can pick the best pre-training task for novel tasks without any labels, and in new domains without any pre-trained networks.
pdf bibtex
```
@inproceedings{Wallace2021Can,
title = {Can We Characterize Tasks Without Labels or Features?},
author = {Bram Wallace and  Ziyang Wu and  Bharath Hariharan},
year = {2021},
booktitle = {CVPR}
}
```
Few-Shot Classification with Feature Map Reconstruction Networks
Davis Wertheimer, Luming Tang, Bharath Hariharan
In CVPR, 2021
TLDR : We assign test images to the training class that yields the best reconstruction of its feature map. This builds in pose invariance, preserves details and leads to state-of-the-art few-shot learning.
pdf project page bibtex
```
@inproceedings{Wertheimer2021FRN,
title = {Few-Shot Classification with Feature Map Reconstruction Networks},
author = {Davis Wertheimer and  Luming Tang and  Bharath Hariharan},
year = {2021},
booktitle = {CVPR}
}
```
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
Jang Hyun Cho, Utkarsh Mall, Kavita Bala, Bharath Hariharan
In CVPR, 2021
TLDR : We can train semantic segmentation models without pixel labels by clustering while encouraging equivariance to geometric transforms and invariance to photometric ones.
pdf project page bibtex
```
@inproceedings{Cho2021PiCIE,
title = {PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering},
author = {Jang Hyun Cho and  Utkarsh Mall and  Kavita Bala and  Bharath Hariharan},
year = {2021},
booktitle = {CVPR}
}
```
Self-training For Few-shot Transfer Across Extreme Task Differences
Cheng Perng Phoo, Bharath Hariharan
In ICLR, 2021 (Oral)
TLDR : We can build state-of-the-art neural feature representations for new domains by (self)training students to replicate pseudo-labels produced by a teacher from another, unrelated problem domain.
pdf bibtex
```
@inproceedings{Phoo2020Self,
title = {Self-training For Few-shot Transfer Across Extreme Task Differences},
author = {Cheng Perng Phoo and  Bharath Hariharan},
year = {2021},
booktitle = {ICLR}
}
```
When Does Self-supervision Improve Few-shot Learning?
Jong-Chyi Su, Subhransu Maji, Bharath Hariharan
In ECCV, 2020
TLDR : An analysis of if and when auxilliary losses based on self-supervision aid representation learning for few-shot learning
pdf project page bibtex
```
@inproceedings{Su2020When,
title = {When Does Self-supervision Improve Few-shot Learning?},
author = {Jong-Chyi Su and  Subhransu Maji and  Bharath Hariharan},
year = {2020},
booktitle = {ECCV}
}
```
Extending and Analyzing Self-Supervised Learning Across Domains
Bram Wallace, Bharath Hariharan
In ECCV, 2020
TLDR : An analysis of how well self-supervision techniques generalize across natural and non-natural domains
pdf bibtex
```
@inproceedings{Wallace2020Extending,
title = {Extending and Analyzing Self-Supervised Learning Across Domains},
author = {Bram Wallace and  Bharath Hariharan},
year = {2020},
booktitle = {ECCV}
}
```
Learning Feature Descriptors using Camera Pose Supervision
Qianqian Wang, Xiaowei Zhou, Bharath Hariharan, Noah Snavely
In ECCV, 2020 (Oral)
TLDR : We learn neural network-based feature descriptors solely from camera pose without any ground truth correspondences
pdf supp project page bibtex
```
@inproceedings{Wang2020Learning,
title = {Learning Feature Descriptors using Camera Pose Supervision},
author = {Qianqian Wang and  Xiaowei Zhou and  Bharath Hariharan and  Noah Snavely},
year = {2020},
booktitle = {ECCV}
}
```
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
Luming Tang, Davis Wertheimer, Bharath Hariharan
In CVPR, 2020
TLDR : Pose normalization by explicitly featurizing the appearance of object parts substantially improves few-shot learning for fine-grained classification problems
pdf supp code bibtex
```
@inproceedings{Tang2020Revisiting,
title = {Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition},
author = {Luming Tang and  Davis Wertheimer and  Bharath Hariharan},
year = {2020},
booktitle = {CVPR}
}
```
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
Yan Wang, Xiangyu Chen, Yurong You, Li Erran Li, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
In CVPR, 2020
TLDR : Domain differences lead to catastrophic failures in 3D object detection. We present a simple and effective remedy.
pdf supp bibtex
```
@inproceedings{Wang2020Train,
title = {Train in Germany, Test in The USA: Making 3D Object Detectors Generalize},
author = {Yan Wang and  Xiangyu Chen and  Yurong You and  Li Erran Li and  Bharath Hariharan and  Mark Campbell and  Kilian Q. Weinberger and  Wei-Lun Chao},
year = {2020},
booktitle = {CVPR}
}
```
Few-Shot Generalization for Single-Image 3D Reconstruction via Prior
Bram Wallace, Bharath Hariharan
In ICCV, 2019
TLDR : A single-view 3D reconstruction system that explicitly leverages the average category shape, and can thus generalize to new classes without training
pdf bibtex
```
@inproceedings{Wallace2019Few-shot,
title = {Few-Shot Generalization for Single-Image 3D Reconstruction via Prior},
author = {Bram Wallace and  Bharath Hariharan},
year = {2019},
booktitle = {ICCV}
}
```
Few-shot Learning with Localization in Realistic Settings
Davis Wertheimer, Bharath Hariharan
In CVPR, 2019 (Oral)
TLDR : We take few-shot learning beyond constrained benchmarks to real-world problems with class imbalance, fine-grained distinctions and potentially additional annotations
pdf code bibtex
```
@inproceedings{Wertheimer2019Few-shot,
title = {Few-shot Learning with Localization in Realistic Settings},
author = {Davis Wertheimer and  Bharath Hariharan},
year = {2019},
booktitle = {CVPR}
}
```
Learning Single-View 3D Reconstruction with Limited Pose Supervision
Guandao Yang, Yin Cui, Serge Belongie, Bharath Hariharan
In ECCV, 2018
TLDR : We explore many ways of reducing the need for ground truth annotations for training 3D reconstruction systems
pdf bibtex
```
@inproceedings{Yang2018Learning,
title = {Learning Single-View 3D Reconstruction with Limited Pose Supervision},
author = {Guandao Yang and  Yin Cui and  Serge Belongie and  Bharath Hariharan},
year = {2018},
booktitle = {ECCV}
}
```
Low-shot Learning from Imaginary Data
Yu-Xiong Wang, Ross Girshick, Martial Herbert, Bharath Hariharan
In CVPR, 2018 (Spotlight)
TLDR : We train few-shot learners in an end-to-end manner to imagine additional training data.
pdf bibtex
```
@inproceedings{Wang2018Low,
title = {Low-shot Learning from Imaginary Data},
author = {Yu-Xiong Wang and  Ross Girshick and  Martial Herbert and  Bharath Hariharan},
year = {2018},
booktitle = {CVPR}
}
```

Low-shot learning with large-scale diffusion
Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou
In CVPR, 2018
TLDR : One of the first explorations into transductive few-shot learning
pdf bibtex

@inproceedings{Douze2018Low,
title = {Low-shot learning with large-scale diffusion},
author = {Matthijs Douze and  Arthur Szlam and  Bharath Hariharan and  Herv\'{e} J\'{e}gou},
year = {2018},
booktitle = {CVPR}
}

Low-shot Visual Recognition by Shrinking and Hallucinating Features
Bharath Hariharan, Ross Girshick
In ICCV, 2017 (Spotlight)
TLDR : We create the first large-scale few-shot learning benchmark and propose two baseline techniques
pdf supp code bibtex
```
@inproceedings{Hariharan2017Low,
title = {Low-shot Visual Recognition by Shrinking and Hallucinating Features},
author = {Bharath Hariharan and  Ross Girshick},
year = {2017},
booktitle = {ICCV}
}
```
Learning Features by Watching Objects Move
Deepak Pathak, Ross Girshick, Piotr Dollár, Trevor Darrell, Bharath Hariharan
In CVPR, 2017
TLDR : A self-supervised learning method that trains neural networks to replicate the output of motion segmentation, but from a single frame, forcing the network to recognize objects that tend to move
pdf project page bibtex
```
@inproceedings{Pathak2017Learning,
title = {Learning Features by Watching Objects Move},
author = {Deepak Pathak and  Ross Girshick and  Piotr Doll\'{a}r and  Trevor Darrell and  Bharath Hariharan},
year = {2017},
booktitle = {CVPR}
}
```

Publications