Search Results for author:
Found 5 papers, 3 papers with code
EPIC Fields: Marrying 3D Geometry and Video Understanding
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.
Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations
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.
NeuralDiff: Segmenting 3D objects that move in egocentric videos
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.
Improving Deep Metric Learning by Divide and Conquer
We propose to build a more expressive representation by jointly splitting the embedding space and the data hierarchically into smaller sub-parts.
Divide and Conquer the Embedding Space for Metric Learning
Approaches for learning a single distance metric often struggle to encode all different types of relationships and do not generalize well.
