Local Metrics for Multi-Object Tracking
Local metrics provide an alternative mechanism to explicitly specify the trade-off between detection and association.
Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection
A novel 3D object detection framework that processes LiDAR data directly on its native range image representation. To overcome scale sensitivity in this perspective view, a range-conditioned dilation (RCD) layer is proposed to dynamically adjust a continuous dilation rate as a function of the measured range. Unparalleled performance is achieved at long range detection when combined with a second stage refinement.
Large Scale Outdoor Scene Reconstruction and Correction with Vision
The BOR2G system developed at the Oxford Robotics Institute fuses data from multiple sensor modalities (cameras, lidars, or both) and regularizes the resulting 3D model. We use a compressed 3D data structure which allows us to operate over a large scale. A earned correction mechanism which takes the global context of the reconstruction and adjusts the constructed mesh addressing pathological errors.
Learning to Drive from Simulation without Real World Labels
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.
Learning to Drive in a Day with Deep Reinforcement Learning
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.
Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing
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.
Deep Cosine Metric Learning for Person Re-Identification
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.
Incremental Adversarial Domain Adaptation for Continually Changing Environments
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.
Meshed Up: Learnt Error Correction in 3D Reconstructions
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.
Hierarchical Attentive Recurrent Tracking
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.
Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation
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.
SORT: Simple Online and Realtime Tracking
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.
Background Modelling with Applications to Visual Object Detection in an Open Pit Mine
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.
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
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.
Fine-Grained Bird Species Recognition via Hierarchical Subset Learning
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.
