1 code implementation • NeurIPS 2023 • Anh Thai, Ahmad Humayun, Stefan Stojanov, Zixuan Huang, Bikram Boote, James M. Rehg
This paper introduces Low-shot Object Learning with Mutual Exclusivity Bias (LSME), the first computational framing of mutual exclusivity bias, a phenomenon commonly observed in infants during word learning.
2 code implementations • ICML 2017 • Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song
Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner.
no code implementations • ICCV 2015 • Ahmad Humayun, Fuxin Li, James M. Rehg
We propose a new energy minimization framework incorporating geodesic distances between segments which solves this problem.
no code implementations • 25 Oct 2015 • S. Hussain Raza, Ahmad Humayun, Matthias Grundmann, David Anderson, Irfan Essa
Our proposed framework provides an efficient approach for finding temporally consistent occlusion boundaries in video by utilizing causality, redundancy in videos, and semantic layout of the scene.
no code implementations • CVPR 2014 • Ahmad Humayun, Fuxin Li, James M. Rehg
By precomputing a graph which can be used for parametric min-cuts over different seeds, we speed up the generation of the segment pool.