Training data for video segmentation are expensive to annotate. This imp...
In this work, we present a novel framework built to simplify 3D asset
ge...
Generalization bounds which assess the difference between the true risk ...
Forecasting of a representation is important for safe and effective auto...
Recently, there has been an increasing interest in building question
ans...
Machine learning advances in the last decade have relied significantly o...
Webpage information extraction (WIE) is an important step to create know...
Communication between embodied AI agents has received increasing attenti...
It is fundamental for personal robots to reliably navigate to a specifie...
We address the problem of visual storytelling, i.e., generating a story ...
While deep reinforcement learning (RL) promises freedom from hand-labele...
Our goal is to forecast the near future given a set of recent observatio...
Many recent datasets contain a variety of different data modalities, for...
Variational autoencoders (VAEs) are one of the powerful likelihood-based...
Why do agents often obtain better reinforcement learning policies when
i...
Autonomous agents must learn to collaborate. It is not scalable to devel...
The first Agriculture-Vision Challenge aims to encourage research in
dev...
In many vision-based reinforcement learning (RL) problems, the agent con...
The success of deep learning in visual recognition tasks has driven
adva...
Using detailed simulations of calorimeter showers as training data, we
i...
For joint inference over multiple variables, a variety of structured
pre...
Diverse and accurate vision+language modeling is an important goal to re...
We propose to learn word embeddings from visual co-occurrences. Two word...
The security of computers is at risk because of information leaking thro...
Dialog is an effective way to exchange information, but subtle details a...
Collaboration is a necessary skill to perform tasks that are beyond one
...
Generative adversarial nets (GANs) and variational auto-encoders have
si...
The recently proposed audio-visual scene-aware dialog task paves the way...
We show that with an appropriate factorization, and encodings of layout ...
Distributed training of deep nets is an important technique to address s...
Data parallelism can boost the training speed of convolutional neural
ne...
Deep structured models are widely used for tasks like semantic segmentat...
Automatically describing an image is an important capability for virtual...
Generative Adversarial Nets (GANs) are very successful at modeling
distr...
Human conversation is a complex mechanism with subtle nuances. It is hen...
Image captioning is an important but challenging task, applicable to vir...
Generative adversarial nets (GANs) are a promising technique for modelin...
Generating diverse questions for given images is an important task for
c...
The need for new methods to deal with big data is a common theme in most...
In this paper we propose a unified framework for structured prediction w...