Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Natural Language to Structured Query Generation via Meta-Learning
In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In…
Neural Motifs: Scene Graph Parsing with Global Context
We investigate the problem of producing structured graph representations of visual scenes. Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We present new…
Neural Poetry Translation
We present the first neural poetry translation system. Unlike previous works that often fail to produce any translation for fixed rhyme and rhythm patterns, our system always translates a source…
SeGAN: Segmenting and Generating the Invisible
Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and…
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new…
Structured Set Matching Networks for One-Shot Part Labeling
Diagrams often depict complex phenomena and serve as a good test bed for visual and textual reasoning. However, understanding diagrams using natural image understanding approaches requires large…
Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension
We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first…
VISIR: Visual and Semantic Image Label Refinement
The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1)content-based image retrieval (BIR), which has traditionally…
What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text
Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated),…
Who Let The Dogs Out? Modeling Dog Behavior From Visual Data
We study the task of directly modelling a visually intelligent agent. Computer vision typically focuses on solving various subtasks related to visual intelligence. We depart from this standard…