209 citations · 709 across the 27 of their papers we have counts for
25 papers · 1 filter
StyleGAN knows Normal, Depth, Albedo, and More
Anand Bhattad, Daniel McKee, Derek Hoiem +1
Intrinsic images, in the original sense, are image-like maps of scene properties like depth, normal, albedo or shading. This paper demonstrates that StyleGAN can easily be induced…
Make It So: Steering StyleGAN for Any Image Inversion and Editing
Anand Bhattad, Viraj Shah, Derek Hoiem +1
StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables…
StyLitGAN: Prompting StyleGAN to Produce New Illumination Conditions
Anand Bhattad, D. A. Forsyth
We propose a novel method, StyLitGAN, for relighting and resurfacing generated images in the absence of labeled data. Our approach generates images with realistic lighting effects,…
SIRfyN: Single Image Relighting from your Neighbors
D. A. Forsyth, Anand Bhattad, Pranav Asthana +2
We show how to relight a scene, depicted in a single image, such that (a) the overall shading has changed and (b) the resulting image looks like a natural image of that scene. Appl…
Cooperating RPN's Improve Few-Shot Object Detection
Weilin Zhang, Yu-Xiong Wang, David A. Forsyth
Learning to detect an object in an image from very few training examples - few-shot object detection - is challenging, because the classifier that sees proposal boxes has very litt…
Cut-and-Paste Object Insertion by Enabling Deep Image Prior for Reshading
Anand Bhattad, David A. Forsyth
We show how to insert an object from one image to another and get realistic results in the hard case, where the shading of the inserted object clashes with the shading of the scene…
Effectively Unbiased FID and Inception Score and where to find them
Min Jin Chong, David Forsyth
This paper shows that two commonly used evaluation metrics for generative models, the Fréchet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected valu…
Improving Style Transfer with Calibrated Metrics
Mao-Chuang Yeh, Shuai Tang, Anand Bhattad +2
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image. We seek to understand how to improve style transfer. To d…
Counterfactual Depth from a Single RGB Image
Theerasit Issaranon, Chuhang Zou, David Forsyth
We describe a method that predicts, from a single RGB image, a depth map that describes the scene when a masked object is removed - we call this "counterfactual depth" that models…
Why do These Match? Explaining the Behavior of Image Similarity Models
Bryan A. Plummer, Mariya I. Vasileva, Vitali Petsiuk +2
Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings. Recent work has primarily focused on explaining models fo…
Unrestricted Adversarial Examples via Semantic Manipulation
Anand Bhattad, Min Jin Chong, Kaizhao Liang +2
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 magn…
Structural Consistency and Controllability for Diverse Colorization
Safa Messaoud, David Forsyth, Alexander G. Schwing
Colorizing a given gray-level image is an important task in the media and advertising industry. Due to the ambiguity inherent to colorization (many shades are often plausible), rec…
Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech
Aditya Deshpande, Jyoti Aneja, Liwei Wang +2
Image captioning is an ambiguous problem, with many suitable captions for an image. To address ambiguity, beam search is the de facto method for sampling multiple captions. However…
An Approximate Shading Model with Detail Decomposition for Object Relighting
Zicheng Liao, Kevin Karsch, Hongyi Zhang +1
We present an object relighting system that allows an artist to select an object from an image and insert it into a target scene. Through simple interactions, the system can adjust…
Quantitative Evaluation of Style Transfer
Mao-Chuang Yeh, Shuai Tang, Anand Bhattad +1
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image. There is a rich literature of variant methods. However, e…
Learning Type-Aware Embeddings for Fashion Compatibility
Mariya I. Vasileva, Bryan A. Plummer, Krishna Dusad +3
Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for…
Detecting Anomalous Faces with 'No Peeking' Autoencoders
Anand Bhattad, Jason Rock, David Forsyth
Detecting anomalous faces has important applications. For example, a system might tell when a train driver is incapacitated by a medical event, and assist in adopting a safe recove…
Standard detectors aren't (currently) fooled by physical adversarial stop signs
Jiajun Lu, Hussein Sibai, Evan Fabry +1
An adversarial example is an example that has been adjusted to produce the wrong label when presented to a system at test time. If adversarial examples existed that could fool a de…
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
Jiajun Lu, Hussein Sibai, Evan Fabry +1
It has been shown that most machine learning algorithms are susceptible to adversarial perturbations. Slightly perturbing an image in a carefully chosen direction in the image spac…
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly
Jiajun Lu, Theerasit Issaranon, David Forsyth
We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples. Our construction suggests some insights into h…
Authoring image decompositions with generative models
Jason Rock, Theerasit Issaranon, Aditya Deshpande +1
We show how to extend traditional intrinsic image decompositions to incorporate further layers above albedo and shading. It is hard to obtain data to learn a multi-layer decomposit…
A Visual Representation for Editing Face Images
Jiajun Lu, Kalyan Sunkavalli, Nathan Carr +2
We propose a new approach for editing face images, which enables numerous exciting applications including face relighting, makeup transfer and face detail editing. Our face edits a…
Learning Diverse Image Colorization
Aditya Deshpande, Jiajun Lu, Mao-Chuang Yeh +2
Colorization is an ambiguous problem, with multiple viable colorizations for a single grey-level image. However, previous methods only produce the single most probable colorization…
CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation
Jiajun Lu, Aditya Deshpande, David Forsyth
Problems such as predicting a new shading field (Y) for an image (X) are ambiguous: many very distinct solutions are good. Representing this ambiguity requires building a condition…