26 citations · 41 across the 5 of their papers we have counts for
8 papers
Learning to Transfer Visual Effects from Videos to Images
Christopher Thomas, Yale Song, Adriana Kovashka
We study the problem of animating images by transferring spatio-temporal visual effects (such as melting) from a collection of videos. We tackle two primary challenges in visual ef…
Preserving Semantic Neighborhoods for Robust Cross-modal Retrieval
Christopher Thomas, Adriana Kovashka
The abundance of multimodal data (e.g. social media posts) has inspired interest in cross-modal retrieval methods. Popular approaches rely on a variety of metric learning losses, w…
Predicting the Politics of an Image Using Webly Supervised Data
Christopher Thomas, Adriana Kovashka
The news media shape public opinion, and often, the visual bias they contain is evident for human observers. This bias can be inferred from how different media sources portray diff…
Artistic Object Recognition by Unsupervised Style Adaptation
Christopher Thomas, Adriana Kovashka
Computer vision systems currently lack the ability to reliably recognize artistically rendered objects, especially when such data is limited. In this paper, we propose a method for…
Persuasive Faces: Generating Faces in Advertisements
Christopher Thomas, Adriana Kovashka
In this paper, we examine the visual variability of objects across different ad categories, i.e. what causes an advertisement to be visually persuasive. We focus on modeling and ge…
Automatic Understanding of Image and Video Advertisements
Zaeem Hussain, Mingda Zhang, Xiaozhong Zhang +5
There is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action. We propose the novel problem o…