Search Results for author: Min Jin Chong

Found 8 papers, 5 papers with code

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval

1 code implementation13 Jul 2021 Min Jin Chong, Wen-Sheng Chu, Abhishek Kumar, David Forsyth

We present Retrieve in Style (RIS), an unsupervised framework for facial feature transfer and retrieval on real images.

GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)

1 code implementation11 Jun 2021 Min Jin Chong, David Forsyth

This adversarial loss guarantees the map is diverse -- a very wide range of anime can be produced from a single content code.

Image-to-Image Translation

Toward Accurate and Realistic Outfits Visualization with Attention to Details

no code implementations CVPR 2021 Kedan Li, Min Jin Chong, Jeffrey Zhang, Jingen Liu

Prior works produce images that are filled with artifacts and fail to capture important visual details necessary for commercial applications.

Image Generation Virtual Try-on

Toward Accurate and Realistic Virtual Try-on Through Shape Matching and Multiple Warps

no code implementations22 Mar 2020 Kedan Li, Min Jin Chong, Jingen Liu, David Forsyth

However, obtaining a realistic image is challenging because the kinematics of garments is complex and because outline, texture, and shading cues in the image reveal errors to human viewers.

Image Generation Virtual Try-on

Effectively Unbiased FID and Inception Score and where to find them

1 code implementation CVPR 2020 Min Jin Chong, David Forsyth

In turn, this effectively bias-free estimate requires good estimates of scores with a finite number of samples.

Unrestricted Adversarial Examples via Semantic Manipulation

1 code implementation ICLR 2020 Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, D. A. Forsyth

Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magnitude of the perturbation.

Colorization Image Captioning +1

EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms

no code implementations NeurIPS 2017 Yogatheesan Varatharajah, Min Jin Chong, Krishnakant Saboo, Brent Berry, Benjamin Brinkmann, Gregory Worrell, Ravishankar Iyer

This paper presents a probabilistic-graphical model that can be used to infer characteristics of instantaneous brain activity by jointly analyzing spatial and temporal dependencies observed in electroencephalograms (EEG).

EEG

Learning Diverse Image Colorization

1 code implementation CVPR 2017 Aditya Deshpande, Jiajun Lu, Mao-Chuang Yeh, Min Jin Chong, David Forsyth

Finally, we build a conditional model for the multi-modal distribution between grey-level image and the color field embeddings.

Colorization

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