10 citations · 21 across the 6 of their papers we have counts for
7 papers · 1 filter
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos
Vadim Tschernezki, Diane Larlus, Iro Laina +1
Computer vision is largely based on 2D techniques, with 3D vision still relegated to a relatively narrow subset of applications. However, by building on recent advances in 3D model…
3D-Aware Instance Segmentation and Tracking in Egocentric Videos
Yash Bhalgat, Vadim Tschernezki, Iro Laina +3
Egocentric videos present unique challenges for 3D scene understanding due to rapid camera motion, frequent object occlusions, and limited object visibility. This paper introduces…
EPIC Fields: Marrying 3D Geometry and Video Understanding
Vadim Tschernezki, Ahmad Darkhalil, Zhifan Zhu +5
Neural rendering is fuelling a unification of learning, 3D geometry and video understanding that has been waiting for more than two decades. Progress, however, is still hampered by…
Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations
Vadim Tschernezki, Iro Laina, Diane Larlus +1
We present Neural Feature Fusion Fields (N3F), a method that improves dense 2D image feature extractors when the latter are applied to the analysis of multiple images reconstructib…
NeuralDiff: Segmenting 3D objects that move in egocentric videos
Vadim Tschernezki, Diane Larlus, Andrea Vedaldi
Given a raw video sequence taken from a freely-moving camera, we study the problem of decomposing the observed 3D scene into a static background and a dynamic foreground containing…
Improving Deep Metric Learning by Divide and Conquer
Artsiom Sanakoyeu, Pingchuan Ma, Vadim Tschernezki +1
Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically simi…
Divide and Conquer the Embedding Space for Metric Learning
Artsiom Sanakoyeu, Vadim Tschernezki, Uta Büchler +1
Learning the embedding space, where semantically similar objects are located close together and dissimilar objects far apart, is a cornerstone of many computer vision applications.…