52 citations · 66 across the 6 of their papers we have counts for
6 papers
Self-supervised Hypergraphs for Learning Multiple World Interpretations
Alina Marcu, Mihai Pirvu, Dragos Costea +5
We present a method for learning multiple scene representations given a small labeled set, by exploiting the relationships between such representations in the form of a multi-task…
Features in Concert: Discriminative Feature Selection meets Unsupervised Clustering
Marius Leordeanu, Alexandra Radu, Rahul Sukthankar
Feature selection is an essential problem in computer vision, important for category learning and recognition. Along with the rapid development of a wide variety of visual features…
Articulated motion discovery using pairs of trajectories
Luca Del Pero, Susanna Ricco, Rahul Sukthankar +1
We propose an unsupervised approach for discovering characteristic motion patterns in videos of highly articulated objects performing natural, unscripted behaviors, such as tigers…
Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep Object Recognition
Marius Leordeanu, Rahul Sukthankar
We propose a general multi-class visual recognition model, termed the Classifier Graph, which aims to generalize and integrate ideas from many of today's successful hierarchical re…
Bayesian Active Distance Metric Learning
Liu Yang, Rong Jin, Rahul Sukthankar
Distance metric learning is an important component for many tasks, such as statistical classification and content-based image retrieval. Existing approaches for learning distance m…
Generalized Boundaries from Multiple Image Interpretations
Marius Leordeanu, Rahul Sukthankar, Cristian Sminchisescu
Boundary detection is essential for a variety of computer vision tasks such as segmentation and recognition. In this paper we propose a unified formulation and a novel algorithm th…