4 citations · 5 across the 9 of their papers we have counts for
9 papers
Robot-Relay : Building-Wide, Calibration-Less Visual Servoing with Learned Sensor Handover Network
Luke Robinson, Matthew Gadd, Paul Newman +1
We present a system which grows and manages a network of remote viewpoints during the natural installation cycle for a newly installed camera network or a newly deployed robot flee…
What you see is what you get: Experience ranking with deep neural dataset-to-dataset similarity for topological localisation
Matthew Gadd, Benjamin Ramtoula, Daniele De Martini +1
Recalling the most relevant visual memories for localisation or understanding a priori the likely outcome of localisation effort against a particular visual memory is useful for ef…
Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN Models
Efimia Panagiotaki, Daniele De Martini, Lars Kunze
In this work, we propose a methodology for investigating the use of semantic attention to enhance the explainability of Graph Neural Network (GNN)-based models. Graph Deep Learning…
SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention
Efimia Panagiotaki, Daniele De Martini, Georgi Pramatarov +2
This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates.…
Roll-Drop: accounting for observation noise with a single parameter
Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala +2
This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to account for observation noi…
Visual DNA: Representing and Comparing Images using Distributions of Neuron Activations
Benjamin Ramtoula, Matthew Gadd, Paul Newman +1
Selecting appropriate datasets is critical in modern computer vision. However, no general-purpose tools exist to evaluate the extent to which two datasets differ. For this, we prop…