226 citations · 750 across the 46 of their papers we have counts for
52 papers
AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art
Faizan Farooq Khan, Diana Kim, Divyansh Jha +5
Discovering the creative potentials of a random signal to various artistic expressions in aesthetic and conceptual richness is a ground for the recent success of generative machine…
Diffusion Guided Domain Adaptation of Image Generators
Kunpeng Song, Ligong Han, Bingchen Liu +2
Can a text-to-image diffusion model be used as a training objective for adapting a GAN generator to another domain? In this paper, we show that the classifier-free guidance can be…
Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models
Diana Kim, Ahmed Elgammal, Marian Mazzone
We present a machine learning system that can quantify fine art paintings with a set of visual elements and principles of art. This formal analysis is fundamental for understanding…
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song +1
Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the f…
Self-Supervised Sketch-to-Image Synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song +1
Imagining a colored realistic image from an arbitrarily drawn sketch is one of the human capabilities that we eager machines to mimic. Unlike previous methods that either requires…
Spatial Frequency Bias in Convolutional Generative Adversarial Networks
Mahyar Khayatkhoei, Ahmed Elgammal
As the success of Generative Adversarial Networks (GANs) on natural images quickly propels them into various real-life applications across different domains, it becomes more and mo…
TIME: Text and Image Mutual-Translation Adversarial Networks
Bingchen Liu, Kunpeng Song, Yizhe Zhu +2
Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I ge…
Sketch-to-Art: Synthesizing Stylized Art Images From Sketches
Bingchen Liu, Kunpeng Song, Ahmed Elgammal
We propose a new approach for synthesizing fully detailed art-stylized images from sketches. Given a sketch, with no semantic tagging, and a reference image of a specific style, th…
2nd Place Solution to the GQA Challenge 2019
Shijie Geng, Ji Zhang, Hang Zhang +2
We present a simple method that achieves unexpectedly superior performance for Complex Reasoning involved Visual Question Answering. Our solution collects statistical features from…
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Bingchen Liu, Yizhe Zhu, Zuohui Fu +2
Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regu…
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning
Yizhe Zhu, Jianwen Xie, Bingchen Liu +1
We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems. It is a generativ…
Graphical Contrastive Losses for Scene Graph Parsing
Ji Zhang, Kevin J. Shih, Ahmed Elgammal +2
Most scene graph parsers use a two-stage pipeline to detect visual relationships: the first stage detects entities, and the second predicts the predicate for each entity pair using…
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Yizhe Zhu, Jianwen Xie, Zhiqiang Tang +2
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes. Existing approaches predominan…
An Interpretable Model for Scene Graph Generation
Ji Zhang, Kevin Shih, Andrew Tao +2
We propose an efficient and interpretable scene graph generator. We consider three types of features: visual, spatial and semantic, and we use a late fusion strategy such that each…
Introduction to the 1st Place Winning Model of OpenImages Relationship Detection Challenge
Ji Zhang, Kevin Shih, Andrew Tao +2
This article describes the model we built that achieved 1st place in the OpenImage Visual Relationship Detection Challenge on Kaggle. Three key factors contribute the most to our s…
Disconnected Manifold Learning for Generative Adversarial Networks
Mahyar Khayatkhoei, Ahmed Elgammal, Maneesh Singh
Natural images may lie on a union of disjoint manifolds rather than one globally connected manifold, and this can cause several difficulties for the training of common Generative A…
Large-Scale Visual Relationship Understanding
Ji Zhang, Yannis Kalantidis, Marcus Rohrbach +3
Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of <subject, relation, object> triples. In real-worl…
The Shape of Art History in the Eyes of the Machine
Ahmed Elgammal, Marian Mazzone, Bingchen Liu +2
How does the machine classify styles in art? And how does it relate to art historians' methods for analyzing style? Several studies have shown the ability of the machine to learn a…
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu +2
Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks(GANs) to gene…
Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication
Ahmed Elgammal, Yan Kang, Milko Den Leeuw
This paper proposes a computational approach for analysis of strokes in line drawings by artists. We aim at developing an AI methodology that facilitates attribution of drawings of…
Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision
Mohamed Elhoseiny, Yizhe Zhu, Han Zhang +1
In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to c…
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
Yizhe Zhu, Ahmed Elgammal
The exponentially increasing use of moving platforms for video capture introduces the urgent need to develop the general background subtraction algorithms with the capability to de…
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny +1
We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the gen…
Overlapping Cover Local Regression Machines
Mohamed Elhoseiny, Ahmed Elgammal
We present the Overlapping Domain Cover (ODC) notion for kernel machines, as a set of overlapping subsets of the data that covers the entire training set and optimized to be spatia…