Search Results for author: Krishna Kumar Singh

Found 16 papers, 5 papers with code

IMAGINE: Image Synthesis by Image-Guided Model Inversion

no code implementations CVPR 2021 Pei Wang, Yijun Li, Krishna Kumar Singh, Jingwan Lu, Nuno Vasconcelos

We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample.

Image Generation

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).

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

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

You Reap What You Sow: Using Videos to Generate High Precision Object Proposals for Weakly-Supervised Object Detection

no code implementations CVPR 2019 Krishna Kumar Singh, Yong Jae Lee

We use the W-RPN to generate high precision object proposals, which are in turn used to re-rank high recall proposals like edge boxes or selective search according to their spatial overlap.

Region Proposal Weakly Supervised Object Detection

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

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.

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

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.

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