211 citations · 941 across the 79 of their papers we have counts for
13 papers · 2 filters
Learning to Segment Moving Objects
Pavel Tokmakov, Cordelia Schmid, Karteek Alahari
We study the problem of segmenting moving objects in unconstrained videos. Given a video, the task is to segment all the objects that exhibit independent motion in at least one fra…
Incremental Learning of Object Detectors without Catastrophic Forgetting
Konstantin Shmelkov, Cordelia Schmid, Karteek Alahari
Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i.e., adapting the original model trained on a set of classes t…
BlitzNet: A Real-Time Deep Network for Scene Understanding
Nikita Dvornik, Konstantin Shmelkov, Julien Mairal +1
Real-time scene understanding has become crucial in many applications such as autonomous driving. In this paper, we propose a deep architecture, called BlitzNet, that jointly perfo…
Weakly-supervised learning of visual relations
Julia Peyre, Ivan Laptev, Cordelia Schmid +1
This paper introduces a novel approach for modeling visual relations between pairs of objects. We call relation a triplet of the form (subject, predicate, object) where the predica…
Detecting Parts for Action Localization
Nicolas Chesneau, Grégory Rogez, Karteek Alahari +1
In this paper, we propose a new framework for action localization that tracks people in videos and extracts full-body human tubes, i.e., spatio-temporal regions localizing actions,…
Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition
Weixin Yang, Terry Lyons, Hao Ni +2
Landmark-based human action recognition in videos is a challenging task in computer vision. One key step is to design a generic approach that generates discriminative features for…