We introduce a self-supervised representation learning method based on the task of temporal alignment between videos. The method trains a network using temporal cycle …
In this paper, we study the problem of semantic annotation on 3D models that are represented as shape graphs. A functional view is taken to represent localized information …
G Donahue, E Elhamifar - … of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
In this paper we tackle the problem of self-supervised video alignment and activity progress prediction using in-the-wild videos. Our proposed self-supervised representation learning …
T Zhou, Y Jae Lee, SX Yu… - Proceedings of the IEEE …, 2015 - openaccess.thecvf.com
Given a set of poorly aligned images of the same visual concept without any annotations, we propose an algorithm to jointly bring them into pixel-wise correspondence by estimating a …
Notions of similarity and correspondence between geometric shapes and images are central to many tasks in geometry processing, computer vision, and computer graphics. The goal of …
The construction of networks of maps among shapes in a collection enables a variety of applications in data-driven geometry processing. A key task in network construction is to …
Background: Knee bone diseases are rare but might be highly destructive. Magnetic Resonance Imaging (MRI) is the main approach to identify knee cancer and its treatment …
Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches …
This paper addresses the problem of automatically localizing dominant objects as spatio- temporal tubes in a noisy collection of videos with minimal or even no supervision. We …