Vadim Tschernezki
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View article: 3D-Aware Instance Segmentation and Tracking in Egocentric Videos
3D-Aware Instance Segmentation and Tracking in Egocentric Videos Open
Egocentric videos present unique challenges for 3D scene understanding due to rapid camera motion, frequent object occlusions, and limited object visibility. This paper introduces a novel approach to instance segmentation and tracking in f…
View article: EPIC Fields: Marrying 3D Geometry and Video Understanding
EPIC Fields: Marrying 3D Geometry and Video Understanding Open
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 a lack of suitable datasets and benchmarks. To address thi…
View article: Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations
Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations Open
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. Given an image feature extractor, for exa…
View article: Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations
Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations Open
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. Given an image feature extractor, for exa…
View article: NeuralDiff: Segmenting 3D objects that move in egocentric videos
NeuralDiff: Segmenting 3D objects that move in egocentric videos Open
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. This task is …
View article: Improving Deep Metric Learning by Divide and Conquer
Improving Deep Metric Learning by Divide and Conquer Open
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 similar objects are located nearby and dissimilar objects far f…
View article: Human-Machine Collaboration for Medical Image Segmentation
Human-Machine Collaboration for Medical Image Segmentation Open
Image segmentation is a ubiquitous step in almost any medical image study. Deep learning-based approaches achieve state-of-the-art in the majority of image segmentation benchmarks. However, end-to-end training of such models requires suffi…
View article: jonpoveda/proxy-nca: v0.0
jonpoveda/proxy-nca: v0.0 Open
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View article: Divide and Conquer the Embedding Space for Metric Learning
Divide and Conquer the Embedding Space for Metric Learning Open
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. Existing approaches usually learn a single metric in the e…