Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Semantic Parsing to Probabilistic Programs for Situated Question Answering
Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using…
Situation Recognition: Visual Semantic Role Labeling for Image Understanding
This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating…
Stating the Obvious: Extracting Visual Common Sense Knowledge
Obtaining common sense knowledge using current information extraction techniques is extremely challenging. In this work, we instead propose to derive simple common sense statements from fully…
Toward a Taxonomy and Computational Models of Abnormalities in Images
The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of…
Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization
Successful online communities (e.g., Wikipedia, Yelp, and StackOverflow) can produce valuable content. However, many communities fail in their initial stages. Starting an online community is…
Unsupervised Deep Embedding for Clustering Analysis
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning…
"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…
What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which…
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…
You Only Look Once: Unified, Real-Time Object Detection
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to…