Cheng Perng Phoo

I am a PhD candidate in the Department of Computer Science at Cornell University, advised by Professor Bharath Hariharan. Prior to Cornell, I received my Bachelor's Degree in Computer Science and Mathematics from the University of Michigan, Ann Arbor.

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Research

My research lies in the intersection of computer vision and machine learning. Specifically, I work on building perception systems that are broadly useful for all problem domains (e.g., remote sensing, medical imagery, self-driving vehicles). Toward this goal, I have identified three major problems: label efficiency (STARTUP, GRAFT), deployment to novel domains (Rote-DA), and trustworthiness. Most of my past works have focused on the first two problems. I am currently investigating how to use multimodal sensory input to improve the label efficiency of perception models and how we could build trustworthy specialist perception models from large-scale frontier models. (See my research statement for more!)

Below is a list of my papers. (*) indicates equal contributions. Representative papers are highlighted.

ground-remote-alignment Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
Utkarsh Mall*, Cheng Perng Phoo*, Meilin Kelsey Liu, Carl Vondrick, Bharath Hariharan, Kavita Bala
International Conference on Learning Representations (ICLR), 2024.

TLDR: We use ground images as intermediary to connect satellite imagery to natural language (encoded using CLIP), yielding VLMs without textual annoations.

pretrain using colorization Pre-training LiDAR-based 3D Object Detectors through Colorization
Tai-Yu Pan, Chenyang Ma, Tianle Chen, Cheng Perng Phoo, Katie Z Luo, Yurong You, Mark Campbell, Kilian Q Weinberger, Bharath Hariharan, Wei-Lun Chao
International Conference on Learning Representations (ICLR), 2024.

TLDR: We pre-train a point cloud detector by tasking it to fill in the missing colors within the point cloud.

reward function for DRIFT Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Katie Z Luo*, Zhenzhen Liu*, Xiangyu Chen*, Yurong You, Sagie Benaim, Cheng Perng Phoo, Mark Campbell, Wen Sun, Bharath Hariharan, Kilian Q. Weinberger
Conference on Neural Information Processing Systems (NEURIPS), 2023.

TLDR: We reframe object discovery as an RL problem and design a reward function to enable faster and more accurate discovery of objects in driving scenes without human supervision.

correspondence from using DIFT Emergent Correspondence from Image Diffusion
Luming Tang*, Menglin Jia*, Qianqian Wang*, Cheng Perng Phoo, Bharath Hariharan
Conference on Neural Information Processing Systems (NEURIPS), 2023.

TLDR: Features from off-the-shelf image diffusion models could be used to identify semantic and geometric correspondence without further training.

distill-from-multiple-source Distilling from Similar Tasks for Transfer Learning on a Budget
Kenneth Borup, Cheng Perng Phoo, Bharath Hariharan
International Conference on Computer Vision (ICCV), 2023.

TLDR: We construct label- and compute-efficient models by identifying and distilling from suitable pre-trained models.

self-training-with-repeated-traversals 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
Conference on Neural Information Processing Systems (NEURIPS), 2022.
Code

TLDR: Unlabeled LiDAR scans from repeated traversals could be used to disambiguate foreground and background objects, yielding cleaner signals for self-training adaptation.

ephemerality Learning to Detect Mobile Objects from LiDAR Scans Without Labels
Yurong You*, Katie Z Luo*, Cheng Perng Phoo, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Code

TLDR: Comparing unlabeled LiDAR scans from multiple traversals on the same location could uncover dynamic LiDAR points that could be used to train a mobile object detector in an unsupervised/self-supervised manner.

transfer_from_synthetic_data Task2Sim: Towards Effective Pre-training and Transfer from Synthetic Data
Samarth Mishra, Rameswar Panda, Cheng Perng Phoo, Chun-Fu (Richard) Chen, Leonid Karlinsky, Kate Saenko, Venkatesh Saligrama, Rogerio S. Feris
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Code

TLDR: Different downstream tasks require different representations pre-trained on synthetic data generated using different configurations (lighting, object poses, etc). We use reinforcement learning to learn a policy that maps a compact task representation to the appropriate synthetic data configuration.

class-taxonomy Coarsely-labeled Data for Better Few-shot Transfer
Cheng Perng Phoo, Bharath Hariharan
International Conference on Computer Vision (ICCV), 2021.
Code

TLDR: Coarsely-labeled data can be cheap to acquire and can be used to learn a better representation for few-shot learning.

meaningful-pseudolabel Self-training for Few-shot Transfer Across Extreme Task Differences
Cheng Perng Phoo, Bharath Hariharan
International Conference on Learning Representations (ICLR) , 2021. Oral (53/2997 Submissions)
Code

TLDR: We can build strong neural representations for novel domains by (self-)training students to replicate pseudo-labels produced by a teacher from another, unrelated problem domain.

pred-concussion Predicting Risk of Sport-Related Concussion in Collegiate Athletes and Military Cadets: A Machine Learning Approach Using Baseline Data from the CARE Consortium Study
Joel Castellanos, Cheng Perng Phoo, James T. Eckner, Lea Franco, Steven P. Broglio, Mike McCrea, Thomas McAllister, Jenna Wiens
Sports Medicine Journal, 2020

TLDR: Baseline tests conducted on college athletes and military cadets before each semester could contain information for identifying athletes/military cadets who are at a higher risk of experiencing a concussion.
classify-cardiogram Heart Sound Classification based on Temporal Alignment Techniques
Jose Javier Gonzalez, Cheng Perng Phoo, Jenna Wiens
Computing In Cardiology (CinC), 2016
Code

TLDR: We use temporal alignment techniques such as dynamic time warping to extract features from heart sound recordings for identifying patients at risk of adverse cardiovascular outcomes.

Awards/Services/Experiences

  • Award: ICCV 2023 Doctoral Consortium
  • Cornell PhD Application Reviewer 2023
  • Peer Review: CVPR 2022, ECCV 2022, CVPR 2023, ICCV 2023, NEURIPS 2023, ICLR 2024
  • Research Internships
  • Teaching Experiences
    • [Cornell] CS4780/5780: Machine Learning for Intelligent Systems [SP18] * Awarded Outstanding Teaching Assistant Award by the Computer Science Department
    • [Cornell] CS4786/5786: Machine Learning for Data Science [FA17]
    • [University of Michigan, Ann Arbor] EECS445: Introduction to Machine Learning [WN17]
    • [University of Michigan, Ann Arbor] EECS203: Discrete Mathematics [FA15|WN16|FA16]

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