Tsung-Yi Lin

I am a senior research scientist at Nvidia Research. I was previously at Google Research, Brain Team.

I work on computer vision and machine learning. I did my PhD at Cornell University and Cornell Tech, where I was advised by Serge Belongie. I did my masters at University California, San Diego and my bachelors at National Taiwan University. I led the creation of the COCO dataset and received the Best Student Paper Award for Focal Loss at ICCV 2017.

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I work on computer vision, machine learning, and robotics. Particularly, I am interested in visual perception (object detection, image segmentation, 3D reconstruction, pose estimation, etc.) and algorithms for learning a general vision model across tasks and domains. Below are recent and selected publications.

Revisiting ResNets: Improved Training and Scaling Strategies
Irwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph
NeurIPS, 2021 (spotlight)

Revisit ResNets with modern scaling and training strategies, showing ResNets are still competitive against modern model architectures.

Multi-Task Self-Training for Learning General Features
Golnaz Ghiasi*, Barret Zoph*, Ekin Dogus Cubuk*, Quoc V. Le, Tsung-Yi Lin,
ICCV, 2021

Apply pseudo labeling to Harness knowledge in multiple datasets/tasks to train one general vision model, achieving competitive results to SoTA on PASCAL, ADE20K, and NYUv2.

Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Image
Weicheng Kuo, Anelia Angelova, Tsung-Yi Lin, Angela Dai
ICCV, 2021

Learning a patch-based image-CAD embedding space for retrieval based 3D reconstruction, improving upon our prior work Mask2CAD.

iNeRF: Inverting Neural Radiance Fields for Pose Estimation
Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Alberto Rodriguez, Phillip Isola, Tsung-Yi Lin,
IROS, 2021
project page / arXiv / video

Given an image of an object and a NeRF of that object, you can estimate that object's pose.

Bottleneck Transformers for Visual Recognition
Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani
CVPR, 2021

Explore a hybrid architecture of CNN and transformer by simply replacing spatial convolutions with self-attention in the final three bottleneck blocks.

Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin Dogus Cubuk, Quoc V. Le, Barret Zoph
CVPR, 2021 (oral)

Study copy-paste augmentation for instance segmentation and demonstrating SoTA performance on COCO and LVIS datasets.

Rethinking Pre-training and Self-training
Barret Zoph* Golnaz Ghiasi*, Tsung-Yi Lin*, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc V. Le
NeurIPS, 2020 (oral)

Compare self-training and pre-training and observe self-training can still improve when pre-training hurts in a region with more labeled data .

Learning to See before Learning to Act: Visual Pre-training for Manipulation
Lin Yen-Chen, Andy Zeng, Shuran Song Phillip Isola, Tsung-Yi Lin
ICRA, 2020
Blog / Video

Leverage visual pre-training from passive observations to aid fast trail-and-error robot learning. Can learn to pick up new objects in ~10 mins.

Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve
Weicheng Kuo, Anelia Angelova, Tsung-Yi Lin, Angela Dai
ECCV, 2020 (spotlight)

Given a single-view image, predict object's 3D shape based on retrieval of CAD models and object pose estimation.

Class-Balanced Loss Based on Effective Number of Samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song Serge Belongie
CVPR, 2019

Propose a benchmark and a simple yet effective class-balanced loss for long-tailed image classification.

DropBlock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le
NeurIPS, 2018

Drop intermediate features randomly during training to regularize learning, working for image classification, object detection, and semantic segmentation.

Focal Loss for Dense Object Detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollar
ICCV, 2017 (best student paper award)

Propose Focal Loss to address fg/bg imbalanced issue in dense object detection. Focal Loss has been adopted beyond object detection since its invention.

Feature Pyramid Networks for Object Detection
Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie
CVPR, 2017

Implement an efficient deep network to bring back the idea of pyramidal representations for object detection.

Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, Larry Zitnick, Piotr Dollar
ECCV, 2014 (oral)

Collecting instance segmentation masks of 80 common objects for training object detection models. The dataset was then extended for panoptic segmentation, multi-modal image-text learning, and beyond.

Area Chair, ICCV 2021

Area Chair, CVPR 2021

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