33 citations · 60 across the 9 of their papers we have counts for
15 papers · 1 filter
Single Domain Generalization via Normalised Cross-correlation Based Convolutions
WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad +2
Deep learning techniques often perform poorly in the presence of domain shift, where the test data follows a different distribution than the training data. The most practically des…
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks
WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad +2
State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains. In this paper, we attempt to unfold an impo…
Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration
Giang Truong, Huu Le, Alvaro Parra +3
With current trends in sensors (cheaper, more volume of data) and applications (increasing affordability for new tasks, new ideas in what 3D data could be useful for); there is cor…
Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning
WeiQin Chuah, Ruwan Tennakoon, Alireza Bab-Hadiashar +1
Learning-based stereo matching and depth estimation networks currently excel on public benchmarks with impressive results. However, state-of-the-art networks often fail to generali…
Consensus Maximisation Using Influences of Monotone Boolean Functions
Ruwan Tennakoon, David Suter, Erchuan Zhang +2
Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level…
Unsupervised Learning for Robust Fitting:A Reinforcement Learning Approach
Giang Truong, Huu Le, David Suter +2
Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for datasets highly contaminated with outliers is, howe…