Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Cross-Sentence Inference for Process Knowledge
For AI systems to reason about real world situations, they need to recognize which processes are at play and which entities play key roles in them. Our goal is to extract this kind of rolebased…
Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference
Random projections have played an important role in scaling up machine learning and data mining algorithms. Recently they have also been applied to probabilistic inference to estimate properties of…
G-CNN: an Iterative Grid Based Object Detector
We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. G-CNN starts with a multi-scale grid of fixed bounding boxes. We train a regressor to move…
Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
We propose Deep3D, a fully automatic 2D-to-3D conversion algorithm that takes 2D images or video frames as input and outputs stereo 3D image pairs. The stereo images can be viewed with 3D glasses or…
FigureSeer: Parsing Result-Figures in Research Papers
‘Which are the pedestrian detectors that yield a precision above 95% at 25% recall?’ Answering such a complex query involves identifying and analyzing the results reported in figures within several…
Much Ado About Time: Exhaustive Annotation of Temporal Data
Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image annotations. These…
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to…
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary…
"What happens if..." Learning to Predict the Effect of Forces in Images
What happens if one pushes a cup sitting on a table toward the edge of the table? How about pushing a desk against a wall? In this paper, we study the problem of understanding the movements of…
A Diagram Is Worth A Dozen Images
Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural…