1 code implementation • NeurIPS 2023 • Vadim Tschernezki, Ahmad Darkhalil, Zhifan Zhu, David Fouhey, Iro Laina, Diane Larlus, Dima Damen, Andrea Vedaldi
Compared to other neural rendering datasets, EPIC Fields is better tailored to video understanding because it is paired with labelled action segments and the recent VISOR segment annotations.
no code implementations • 7 Sep 2022 • Vadim Tschernezki, Iro Laina, Diane Larlus, Andrea Vedaldi
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 reconstructible as a 3D scene.
no code implementations • 19 Oct 2021 • 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 the objects that move in the video sequence.
1 code implementation • 9 Sep 2021 • Artsiom Sanakoyeu, Pingchuan Ma, Vadim Tschernezki, Björn Ommer
We propose to build a more expressive representation by jointly splitting the embedding space and the data hierarchically into smaller sub-parts.
1 code implementation • CVPR 2019 • Artsiom Sanakoyeu, Vadim Tschernezki, Uta Büchler, Björn Ommer
Approaches for learning a single distance metric often struggle to encode all different types of relationships and do not generalize well.