89 citations · 533 across the 28 of their papers we have counts for
7 papers · 1 filter
A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
Licheng Yu, Hao Tan, Mohit Bansal +1
Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a unified framework for the tasks of referrin…
Combining Multiple Cues for Visual Madlibs Question Answering
Tatiana Tommasi, Arun Mallya, Bryan Plummer +3
This paper presents an approach for answering fill-in-the-blank multiple choice questions from the Visual Madlibs dataset. Instead of generic and commonly used representations trai…
Detailed Garment Recovery from a Single-View Image
Shan Yang, Tanya Ambert, Zherong Pan +4
Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs…
Learning Temporal Transformations From Time-Lapse Videos
Yipin Zhou, Tamara L. Berg
Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in…
When was that made?
Sirion Vittayakorn, Alexander C. Berg, Tamara L. Berg
In this paper, we explore deep learning methods for estimating when objects were made. Automatic methods for this task could potentially be useful for historians, collectors, or an…
Solving Visual Madlibs with Multiple Cues
Tatiana Tommasi, Arun Mallya, Bryan Plummer +3
This paper focuses on answering fill-in-the-blank style multiple choice questions from the Visual Madlibs dataset. Previous approaches to Visual Question Answering (VQA) have mainl…
Modeling Context in Referring Expressions
Licheng Yu, Patrick Poirson, Shan Yang +2
Humans refer to objects in their environments all the time, especially in dialogue with other people. We explore generating and comprehending natural language referring expressions…