Scene understanding is a prerequisite to many high level tasks for any automated intelligent
machine operating in real world environments. Recent attempts with supervised learning …

We describe a new spatio-temporal video autoencoder, based on a classic spatial image
autoencoder and a novel nested temporal autoencoder. The temporal encoder is represented …

Building Information Models (BIMs) are becoming the official standard in the construction
industry for encoding, reusing, and exchanging information about structural assets. …

We introduce a challenging decision-making task that we call active acquisition for
multimodal temporal data (A2MT). In many real-world scenarios, input features are not readily …

We propose a novel multimodal video benchmark-the Perception Test-to evaluate the perception
and reasoning skills of pre-trained multimodal models (eg Flamingo, BEiT-3, or GPT-4). …

Most successful self-supervised learning methods are trained to align the representations of
two independent views from the data. State-of-the-art methods in video are inspired by …

Understanding long real-world videos requires modeling of long-range visual dependencies.
To this end we explore video-first architectures building on the common paradigm of …

We propose a combined line segment and elliptical arc detector, which formally guarantees
the control of the number of false positives and requires no parameter tuning. The accuracy …

We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between
classic geometric computer vision and deep learning. Inspired by the recent success of …

We introduce SceneNet, a framework for generating high-quality annotated 3D scenes to
aid indoor scene understanding. SceneNet leverages manually-annotated datasets of real …