Search Results for author: Yong Jae Lee

Found 36 papers, 18 papers with code

Few-shot Image Generation via Cross-domain Correspondence

no code implementations CVPR 2021 Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang

Training generative models, such as GANs, on a target domain containing limited examples (e. g., 10) can easily result in overfitting.

Image Generation

Progressive Temporal Feature Alignment Network for Video Inpainting

1 code implementation CVPR 2021 Xueyan Zou, Linjie Yang, Ding Liu, Yong Jae Lee

To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content.

Optical Flow Estimation Video Inpainting

Generating Furry Cars: Disentangling Object Shape & Appearance across Multiple Domains

no code implementations5 Apr 2021 Utkarsh Ojha, Krishna Kumar Singh, Yong Jae Lee

We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e. g., dogs and cars).

Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains

no code implementations ICLR 2021 Utkarsh Ojha, Krishna Kumar Singh, Yong Jae Lee

We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e. g., dogs and cars).

YolactEdge: Real-time Instance Segmentation on the Edge

2 code implementations22 Dec 2020 Haotian Liu, Rafael A. Rivera Soto, Fanyi Xiao, Yong Jae Lee

We propose YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds.

Real-time Instance Segmentation Semantic Segmentation

Delving Deeper into Anti-aliasing in ConvNets

2 code implementations21 Aug 2020 Xueyan Zou, Fanyi Xiao, Zhiding Yu, Yong Jae Lee

Aliasing refers to the phenomenon that high frequency signals degenerate into completely different ones after sampling.

Instance Segmentation Semantic Segmentation

YOLACT++: Better Real-time Instance Segmentation

33 code implementations3 Dec 2019 Daniel Bolya, Chong Zhou, Fanyi Xiao, Yong Jae Lee

Then we produce instance masks by linearly combining the prototypes with the mask coefficients.

Ranked #3 on Real-time Instance Segmentation on MSCOCO (using extra training data)

Real-time Instance Segmentation Semantic Segmentation

MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation

4 code implementations CVPR 2020 Yuheng Li, Krishna Kumar Singh, Utkarsh Ojha, Yong Jae Lee

We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation.

Conditional Image Generation

Password-conditioned Anonymization and Deanonymization with Face Identity Transformers

1 code implementation26 Nov 2019 Xiuye Gu, Weixin Luo, Michael S. Ryoo, Yong Jae Lee

Cameras are prevalent in our daily lives, and enable many useful systems built upon computer vision technologies such as smart cameras and home robots for service applications.

Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data

no code implementations NeurIPS 2020 Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee

We propose a novel unsupervised generative model that learns to disentangle object identity from other low-level aspects in class-imbalanced data.

Representation Learning

FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery

2 code implementations CVPR 2019 Krishna Kumar Singh, Utkarsh Ojha, Yong Jae Lee

We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories.

Conditional Image Generation Fine-Grained Visual Categorization +1

DOCK: Detecting Objects by transferring Common-sense Knowledge

no code implementations ECCV 2018 Krishna Kumar Singh, Santosh Divvala, Ali Farhadi, Yong Jae Lee

We present a scalable approach for Detecting Objects by transferring Common-sense Knowledge (DOCK) from source to target categories.

Common Sense Reasoning Semantic Similarity +2

Learning to Anonymize Faces for Privacy Preserving Action Detection

1 code implementation ECCV 2018 Zhongzheng Ren, Yong Jae Lee, Michael S. Ryoo

The end result is a video anonymizer that performs pixel-level modifications to anonymize each person's face, with minimal effect on action detection performance.

Action Detection

Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks

no code implementations25 May 2017 Wenjian Hu, Krishna Kumar Singh, Fanyi Xiao, Jinyoung Han, Chen-Nee Chuah, Yong Jae Lee

Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest.

Weakly-supervised Visual Grounding of Phrases with Linguistic Structures

no code implementations CVPR 2017 Fanyi Xiao, Leonid Sigal, Yong Jae Lee

We propose a weakly-supervised approach that takes image-sentence pairs as input and learns to visually ground (i. e., localize) arbitrary linguistic phrases, in the form of spatial attention masks.

Visual Grounding

Identifying First-person Camera Wearers in Third-person Videos

no code implementations CVPR 2017 Chenyou Fan, Jang-Won Lee, Mingze Xu, Krishna Kumar Singh, Yong Jae Lee, David J. Crandall, Michael S. Ryoo

We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in environments in which multiple people are wearing body-worn cameras while a third-person static camera also captures the scene.

Activity Recognition Object Tracking +1

Interspecies Knowledge Transfer for Facial Keypoint Detection

1 code implementation CVPR 2017 Maheen Rashid, Xiuye Gu, Yong Jae Lee

Instead of directly finetuning a network trained to detect keypoints on human faces to animal faces (which is sub-optimal since human and animal faces can look quite different), we propose to first adapt the animal images to the pre-trained human detection network by correcting for the differences in animal and human face shape.

Human Detection Keypoint Detection +1

End-to-End Localization and Ranking for Relative Attributes

no code implementations9 Aug 2016 Krishna Kumar Singh, Yong Jae Lee

We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons.

Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals

no code implementations CVPR 2016 Fanyi Xiao, Yong Jae Lee

We present an unsupervised approach that generates a diverse, ranked set of bounding box and segmentation video object proposals---spatio-temporal tubes that localize the foreground objects---in an unannotated video.

Discovering the Spatial Extent of Relative Attributes

no code implementations ICCV 2015 Fanyi Xiao, Yong Jae Lee

We present a weakly-supervised approach that discovers the spatial extent of relative attributes, given only pairs of ordered images.

FlowWeb: Joint Image Set Alignment by Weaving Consistent, Pixel-Wise Correspondences

no code implementations CVPR 2015 Tinghui Zhou, Yong Jae Lee, Stella X. Yu, Alyosha A. Efros

Given a set of poorly aligned images of the same visual concept without any annotations, we propose an algorithm to jointly bring them into pixel-wise correspondence by estimating a FlowWeb representation of the image set.

Optical Flow Estimation

Predicting Important Objects for Egocentric Video Summarization

no code implementations18 May 2015 Yong Jae Lee, Kristen Grauman

Our results on two egocentric video datasets show the method's promise relative to existing techniques for saliency and summarization.

Event Detection Video Summarization

Weakly-supervised Discovery of Visual Pattern Configurations

no code implementations NeurIPS 2014 Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell

The increasing prominence of weakly labeled data nurtures a growing demand for object detection methods that can cope with minimal supervision.

Object Detection

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