16 citations · 27 across the 5 of their papers we have counts for
6 papers
Answer-Me: Multi-Task Open-Vocabulary Visual Question Answering
AJ Piergiovanni, Wei Li, Weicheng Kuo +3
We present Answer-Me, a task-aware multi-task framework which unifies a variety of question answering tasks, such as, visual question answering, visual entailment, visual reasoning…
FindIt: Generalized Localization with Natural Language Queries
Weicheng Kuo, Fred Bertsch, Wei Li +3
We propose FindIt, a simple and versatile framework that unifies a variety of visual grounding and localization tasks including referring expression comprehension, text-based local…
SyntheticFur dataset for neural rendering
Trung Le, Ryan Poplin, Fred Bertsch +2
We introduce a new dataset called SyntheticFur built specifically for machine learning training. The dataset consists of ray traced synthetic fur renders with corresponding rasteri…
Learning via social awareness: Improving a deep generative sketching model with facial feedback
Natasha Jaques, Jennifer McCleary, Jesse Engel +4
In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards.…
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Amélie Royer, Konstantinos Bousmalis, Stephan Gouws +4
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latte…
Improving image generative models with human interactions
Andrew Kyle Lampinen, David So, Douglas Eck +1
GANs provide a framework for training generative models which mimic a data distribution. However, in many cases we wish to train these generative models to optimize some auxiliary…