Alex Bewley
A method for transferring a vision-based lane following driving policy from simulation to operation on a rural road without any real-world labels. Our approach leverages recent advances in image-to-image translation to achieve domain transfer while jointly learning a single-camera control policy from simulation control labels.
This work demonstrates model-free deep reinforcement learning on an autonomous car in the real world. With a handful of exploration and optimisation steps performed on the single onboard NVIDIA DRIVE PX2, our model-free algorithm learnt to follow its lane without any prior map.
This work unifies auxiliary tasks, adversarial information removal and side tasks analysis with a single multi-task learning framework we call neural stethoscopes. Neural stethoscopes are then used to interrogate specific visual cues a network learns in the context of intuitive physics. Furthermore, we are able to actively de-bias network predictions as well as enhance performance via suitable auxiliary and adversarial stethoscope losses.
This work presents a method for learning a feature embedding where the cosine similarity is effectively optimised through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped of the Network, facilitating nearest neighbour queries on unseen individuals using the cosine similarity metric.
Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. Unsupervised domain adaptation aims to address this challenge, though current approaches do not utilise the continuity of the occurring shifts. This work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from unsupervised alignment to a series of sub-domains which successively diverge from the labelled source domain.
Dense reconstructions often contain errors that prior work has so far minimised using high quality sensors and regularising the output. Nevertheless, errors still persist. This paper proposes a machine learning technique to identify errors in three dimensional (3D) meshes. Beyond simply identifying errors, our method quantifies both the magnitude and the direction of depth estimate errors when viewing the scene.
Inspired by how the human visual cortex employs spatial attention and separate “where” and “what” processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos.
Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. This work develops a framework for applying adversarial techniques to adapt popular, state-of-the-art network architectures with the additional objective to be invariant across conditions.
This work presents a fast, yet simple, technique for updating trajectory estimates within an online multiple object tracking framework. Furthermore, the impact of detection quality on tracking is highlighted by achieving stat-of-the-art performance on a recent tracking benchmark.
This work investigates the use of appearance based object detection in an open pit mine. Various forms of background modelling techniques are explored for adapting a pretrained detector to the novel environment.
A novel deep convolutional neural network (DCNN) architecture is proposed for fine-grained image classification. This architecture, called MixDCNN, combines the output of several DCNNs within a mixture model framework and is shown to outperform other methods.
This paper presents a novel method to improve fine-grained classification based on hierarchical subset learning. First a similarity tree is formed where classes with strong visual correlations are grouped into subsets. An expert local classifier with strong discriminative power to distinguish visually similar classes is then learnt for each subset.
