Robots that learn and improve
Robots that learn and improve
Visual learning has enormous potential to solve previously impossible problems in machine perception. The recent deep learning breakthrough from the machine learning community has allowed researchers not only to address new visual learning problems, but also to solve old problems. In general, the success of deep learning is attributed to the vast computational resources available and large annotated datasets containing millions of images. In spite of the excitement generated by these recent developments, there is a lack of understanding of how deep learning works, which invites questions about convergence, stability and robustness of such models. This program addresses important challenges in deep learning, such as: effective transfer learning, role of probabilistic graphical models in deep learning, efficient training and inference algorithms, etc. Answering these questions will allow us to design and implement robust visual learning systems that will help our robots fully understand the environment around them.
Gustavo Carneiro, Chunhua Shen, Tom Drummond, Rafael Felix, Tong Shen, Vladimir Nekrasov, Ming Cai, Benjamin Meyer, Ben Harwood, Yan Zuo, Gil Avraham, Luis Guerra, Adrian Johnston
This program explores the enormous potential that still exists towards solving previously impossible problems in machine perception. The recent breakthroughs from the machine learning community have allowed researchers to address new visual learning problems, as well as solve old problems. It addresses the important challenges in deep learning, such as effective transfer learning, the role of probabilistic graphical models in deep learning, and efficient training and inference algorithms. Answering these questions will allow us to design and implement strong visual learning systems that will help robots to understand the environment around them.
Chunhua Shen, Gustavo Carneiro, Vijay Kumar, Ian Reid, Chao Ma, Benjamin Meyer, Tong Shen, Hui Li, Yuchao Jiang, Bohan Zhuang
Learning that is specific to robotic vision tasks where there are resource constraints (embedded vision system). Video segmentation (i.e. image segmentation for video, applied to static scenes / moving camera, and general scenes with unknown motion; DL suitable for deployment on storage and power constrained embedded systems (eg COTSbot); Fast, approximate, and asymmetrically computed inference; Robust inference (via understanding failure modes); Unsupervised learning; Online and lifelong learning for robotic vision; “Any-time” algorithms.
July 2016 - 2017
Vijay Kumar, Gustavo Carneiro, Chunhua Shen, Ian Reid, Basura Fernando, Jian “Edison” Guo, Ben Harwood, Yan Zuo, Rafael Felix, Adrian Johnston
It is essential that the Centre be active at the forefront of current machine learning techniques. To explore, develop and exploit novel network architectures; to develop detection and instance level/pixel level annotations for 1000s classes and open sets of classes. To develop efficient and/or weakly supervised and/or online trained and/or unsupervised and/or zero-shot learning models. Active learning with and from temporal data.
Australian Centre for Robotic Vision
2 George Street Brisbane, 4001
+61 7 3138 7549