108 citations · 417 across the 27 of their papers we have counts for
18 papers · 1 filter
GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors
Joshua Knights, Stephen Hausler, Sridha Sridharan +2
LiDAR place recognition approaches based on deep learning suffer from significant performance degradation when there is a shift between the distribution of training and test datase…
Subspace Distillation for Continual Learning
Kaushik Roy, Christian Simon, Peyman Moghadam +1
An ultimate objective in continual learning is to preserve knowledge learned in preceding tasks while learning new tasks. To mitigate forgetting prior knowledge, we propose a novel…
Learning Partial Correlation based Deep Visual Representation for Image Classification
Saimunur Rahman, Piotr Koniusz, Lei Wang +3
Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolut…
Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization
Kavisha Vidanapathirana, Peyman Moghadam, Sridha Sridharan +1
In large-scale metric localization, an incorrect result during retrieval will lead to an incorrect pose estimate or loop closure. Re-ranking methods propose to take into account al…
Uncertainty-Aware Lidar Place Recognition in Novel Environments
Keita Mason, Joshua Knights, Milad Ramezani +2
State-of-the-art lidar place recognition models exhibit unreliable performance when tested on environments different from their training dataset, which limits their use in complex…
What's in the Black Box? The False Negative Mechanisms Inside Object Detectors
Dimity Miller, Peyman Moghadam, Mark Cox +2
In object detection, false negatives arise when a detector fails to detect a target object. To understand why object detectors produce false negatives, we identify five 'false nega…
InCloud: Incremental Learning for Point Cloud Place Recognition
Joshua Knights, Peyman Moghadam, Milad Ramezani +2
Place recognition is a fundamental component of robotics, and has seen tremendous improvements through the use of deep learning models in recent years. Networks can experience sign…
Point Cloud Segmentation Using Sparse Temporal Local Attention
Joshua Knights, Peyman Moghadam, Clinton Fookes +1
Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment. However despite…
The CSIRO Crown-of-Thorn Starfish Detection Dataset
Jiajun Liu, Brano Kusy, Ross Marchant +17
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attemp…
LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition
Kavisha Vidanapathirana, Milad Ramezani, Peyman Moghadam +2
Retrieval-based place recognition is an efficient and effective solution for re-localization within a pre-built map, or global data association for Simultaneous Localization and Ma…
DeepSeagrass Dataset
Scarlett Raine, Ross Marchant, Peyman Moghadam +3
We introduce a dataset of seagrass images collected by a biologist snorkelling in Moreton Bay, Queensland, Australia, as described in our publication: arXiv:2009.09924. The images…
Multi-species Seagrass Detection and Classification from Underwater Images
Scarlett Raine, Ross Marchant, Peyman Moghadam +3
Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images. Manual review of these images to extract ecologi…
Scalable learning for bridging the species gap in image-based plant phenotyping
Daniel Ward, Peyman Moghadam
The traditional paradigm of applying deep learning -- collect, annotate and train on data -- is not applicable to image-based plant phenotyping as almost 400,000 different plant sp…
Temporally Coherent Embeddings for Self-Supervised Video Representation Learning
Joshua Knights, Ben Harwood, Daniel Ward +3
This paper presents TCE: Temporally Coherent Embeddings for self-supervised video representation learning. The proposed method exploits inherent structure of unlabeled video data t…
Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
Shafeeq Elanattil, Peyman Moghadam
We introduce a synthetic dataset for evaluating non-rigid 3D human reconstruction based on conventional RGB-D cameras. The dataset consist of seven motion sequences of a single hum…
Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
Shafeeq Elanattil, Peyman Moghadam, Simon Denman +2
This paper presents a method which can track and 3D reconstruct the non-rigid surface motion of human performance using a moving RGB-D camera. 3D reconstruction of marker-less huma…
Deep Leaf Segmentation Using Synthetic Data
Daniel Ward, Peyman Moghadam, Nicolas Hudson
Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping. Applying state-of-the…
Non-rigid Reconstruction with a Single Moving RGB-D Camera
Shafeeq Elanattil, Peyman Moghadam, Sridha Sridharan +2
We present a novel non-rigid reconstruction method using a moving RGB-D camera. Current approaches use only non-rigid part of the scene and completely ignore the rigid background.…