129 citations · 307 across the 13 of their papers we have counts for
6 papers · 1 filter
A Bayesian Data Augmentation Approach for Learning Deep Models
Toan Tran, Trung Pham, Gustavo Carneiro +2
Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on…
Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge
S. Wade-McCue, N. Kelly-Boxall, M. McTaggart +19
We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the…
Semantic Segmentation from Limited Training Data
A. Milan, T. Pham, K. Vijay +22
We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transpa…
SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes
Trung Pham, Thanh-Toan Do, Niko Sünderhauf +1
This paper presents SceneCut, a novel approach to jointly discover previously unseen objects and non-object surfaces using a single RGB-D image. SceneCut's joint reasoning over sce…
Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge
D. Morrison, A. W. Tow, M. McTaggart +19
The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. Th…
Simultaneous Feature Aggregating and Hashing for Large-scale Image Search
Thanh-Toan Do, Dang-Khoa Le Tan, Trung T. Pham +1
In most state-of-the-art hashing-based visual search systems, local image descriptors of an image are first aggregated as a single feature vector. This feature vector is then subje…