926 citations · 2.2k across the 54 of their papers we have counts for
8 papers · 1 filter
Artificial Dummies for Urban Dataset Augmentation
Antonín Vobecký, David Hurych, Michal Uřičář +2
Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are…
Just Ask: Learning to Answer Questions from Millions of Narrated Videos
Antoine Yang, Antoine Miech, Josef Sivic +2
Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and preve…
Learning Object Manipulation Skills via Approximate State Estimation from Real Videos
Vladimír Petrík, Makarand Tapaswi, Ivan Laptev +1
Humans are adept at learning new tasks by watching a few instructional videos. On the other hand, robots that learn new actions either require a lot of effort through trial and err…
CosyPose: Consistent multi-view multi-object 6D pose estimation
Yann Labbé, Justin Carpentier, Mathieu Aubry +1
We introduce an approach for recovering the 6D pose of multiple known objects in a scene captured by a set of input images with unknown camera viewpoints. First, we present a singl…
RareAct: A video dataset of unusual interactions
Antoine Miech, Jean-Baptiste Alayrac, Ivan Laptev +2
This paper introduces a manually annotated video dataset of unusual actions, namely RareAct, including actions such as "blend phone", "cut keyboard" and "microwave shoes". RareAct…
Identifying Heavy-Flavor Jets Using Vectors of Locally Aggregated Descriptors
Jana Bielčíková, Raghav Kunnawalkam Elayavalli, Georgy Ponimatkin +2
Jets of collimated particles serve a multitude of purposes in high energy collisions. Recently, studies of jet interaction with the quark-gluon plasma (QGP) created in high energy…
Occlusion resistant learning of intuitive physics from videos
Ronan Riochet, Josef Sivic, Ivan Laptev +1
To reach human performance on complex tasks, a key ability for artificial systems is to understand physical interactions between objects, and predict future outcomes of a situation…
Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions
Ignacio Rocco, Relja Arandjelović, Josef Sivic
In this work we target the problem of estimating accurately localised correspondences between a pair of images. We adopt the recent Neighbourhood Consensus Networks that have demon…