activity
20172020
most citedLocus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling

86 citations · 184 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2020★ 53 cited

Delta Descriptors: Change-Based Place Representation for Robust Visual Localization

Sourav Garg, Ben Harwood, Gaurangi Anand +1

Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheri…

cs.CV2020★ 1 cited

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…

cs.CV2019

Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space

Rafael Felix, Ben Harwood, Michele Sasdelli +1

Generalised zero-shot learning (GZSL) is a classification problem where the learning stage relies on a set of seen visual classes and the inference stage aims to identify both the…

cs.CV2019★ 1 cited

Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces

Rafael Felix, Ben Harwood, Michele Sasdelli +1

Generalised zero-shot learning (GZSL) methods aim to classify previously seen and unseen visual classes by leveraging the semantic information of those classes. In the context of G…

cs.CV2017

Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

Benjamin J. Meyer, Ben Harwood, Tom Drummond

We present a Gaussian kernel loss function and training algorithm for convolutional neural networks that can be directly applied to both distance metric learning and image classifi…

cs.CV2017★ 43 cited

Smart Mining for Deep Metric Learning

Ben Harwood, Vijay Kumar B G, Gustavo Carneiro +2

To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples f…