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Leveraging Structure for Generalization and Prediction in Visual System

Yufei Ye
Master's Thesis, Tech. Report, CMU-RI-TR-19-70, June, 2019

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Abstract

Our surrounding world is highly structured. Humans have a great capacity of understanding and leveraging those structures to generalize to novel scenarios and to predict the future. The thesis studies how computer vision systems benefit from a similar process — leveraging inherent structures in data to improve generalization and prediction capacity.

It focuses on two specific aspects: zero-shot recognition using categorical structures which is explicitly specified by knowledge graphs; video predictions by leveraging the implicit physical structures among entities. Both methods are based on the scalable machine learning framework, graph neural network, to directly learn structures from large-scale data. In zero-shot recognition, we have shown that accuracy improves significantly and is more robust due to external knowledge in the knowledge graph. In video prediction, we have found the long-term prediction is significantly sharper when factoring the structure among entities.

Notes
This work serves as the master thesis of Yufei Ye.

@mastersthesis{Ye-2019-117140,
author = {Yufei Ye},
title = {Leveraging Structure for Generalization and Prediction in Visual System},
year = {2019},
month = {June},
school = {},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-19-70},
} 2019-08-12T13:41:46-04:00